1. The document discusses different levels of link analysis on the web, including macroscopic, microscopic, and mesoscopic views.
2. It presents methods for calculating PageRank and functional rankings through various damping functions like exponential and linear damping.
3. The recursive formulation of linear damping is also described to allow computation without storing the full link matrix in memory.
This document discusses a method for detecting dishonest behaviors in online networks using graph-based ranking techniques. It proposes a technique called PolaritySpam that detects web spam by propagating a-priori spam and not-spam scores for web pages using a PageRank-based algorithm. This allows it to incorporate content-based heuristics to select the initial set of spam and not-spam pages. The method is evaluated on a large web spam dataset and is shown to outperform the baseline TrustRank approach by better demoting spam pages. The document also discusses how similar graph-based techniques could be applied to detect untrustworthy users in social networks by computing positive and negative reputation scores.
Enhancement in Weighted PageRank Algorithm Using VOLIOSR Journals
1) The document proposes enhancing the Weighted PageRank algorithm by incorporating visits of links (VOL) to calculate page rank. It takes into account both the number of visits of inlinks and outlinks of pages.
2) A new algorithm, called Enhanced Weighted PageRank using VOL (EWPR VOL), is presented. It calculates page popularity based on the number of visits of inlinks (WVOLin) and outlinks (WVOLout).
3) The EWPR VOL algorithm is demonstrated using a sample web graph to calculate page rank values for pages A, B and C based on the number of visits of their inlinks and outlinks.
This lecture discusses the structure of the web, link analysis, and web search. It covers the basic components of a search engine including crawling, indexing, ranking, and query processing. It describes how web crawlers work by recursively fetching links from seed URLs. It also discusses link-based ranking algorithms like PageRank that rank pages based on the link structure of the web. The lecture further covers challenges like spam and approaches to detect web spam like TrustRank, Anti-TrustRank, Spam Mass, and Link Farm spam. The author proposes techniques to refine seed sets and order algorithms to improve web spam filtering.
The team at Prosperity Media has put together a presentation for Mays Online Marketing Sydney event around the topic of - Google Penalty Recovery & Analysis. If your website has noted a traffic drop it is worth viewing the content in this presentation.
This document discusses PageRank and related methods for ranking web pages. It provides an overview that includes: what PageRank is based on links as votes; algorithms for calculating PageRank using a simplified method and damping factor; different PageRank values from actual, toolbars, and ODP; manipulating PageRank ratings; and the relationship between PageRank and the semantic web. It also mentions two similar works from 1998 - Kleinberg's authoritative sources algorithm and Chakrabarti's experiments in topic distillation which added factors to PageRank.
This document summarizes a lecture on graph algorithms and PageRank using MapReduce. It discusses representing graphs in MapReduce, performing breadth-first search, finding shortest paths, and calculating PageRank through an iterative process of redistributing PageRank values along edges in the graph. The PageRank algorithm is broken into phases that map nodes to PageRank fragments, reduce to calculate new PageRank values, and iterate until convergence is reached. While MapReduce has limitations for iterative algorithms, this approach allows processing graph partitions in parallel through multiple MapReduce jobs.
Larry Page and Sergey Brin developed the PageRank algorithm at Stanford University in 1998. PageRank is the backbone of Google's search engine technology. It ranks web pages based on the number and quality of inbound links, with the assumption that more important pages receive more links. The algorithm calculates a probability distribution over web pages to determine each page's importance. It works iteratively until the PageRank values settle close to 1 across all pages. However, PageRank faces issues like preferring older pages and being vulnerable to manipulation through link spamming techniques.
This document discusses a method for detecting dishonest behaviors in online networks using graph-based ranking techniques. It proposes a technique called PolaritySpam that detects web spam by propagating a-priori spam and not-spam scores for web pages using a PageRank-based algorithm. This allows it to incorporate content-based heuristics to select the initial set of spam and not-spam pages. The method is evaluated on a large web spam dataset and is shown to outperform the baseline TrustRank approach by better demoting spam pages. The document also discusses how similar graph-based techniques could be applied to detect untrustworthy users in social networks by computing positive and negative reputation scores.
Enhancement in Weighted PageRank Algorithm Using VOLIOSR Journals
1) The document proposes enhancing the Weighted PageRank algorithm by incorporating visits of links (VOL) to calculate page rank. It takes into account both the number of visits of inlinks and outlinks of pages.
2) A new algorithm, called Enhanced Weighted PageRank using VOL (EWPR VOL), is presented. It calculates page popularity based on the number of visits of inlinks (WVOLin) and outlinks (WVOLout).
3) The EWPR VOL algorithm is demonstrated using a sample web graph to calculate page rank values for pages A, B and C based on the number of visits of their inlinks and outlinks.
This lecture discusses the structure of the web, link analysis, and web search. It covers the basic components of a search engine including crawling, indexing, ranking, and query processing. It describes how web crawlers work by recursively fetching links from seed URLs. It also discusses link-based ranking algorithms like PageRank that rank pages based on the link structure of the web. The lecture further covers challenges like spam and approaches to detect web spam like TrustRank, Anti-TrustRank, Spam Mass, and Link Farm spam. The author proposes techniques to refine seed sets and order algorithms to improve web spam filtering.
The team at Prosperity Media has put together a presentation for Mays Online Marketing Sydney event around the topic of - Google Penalty Recovery & Analysis. If your website has noted a traffic drop it is worth viewing the content in this presentation.
This document discusses PageRank and related methods for ranking web pages. It provides an overview that includes: what PageRank is based on links as votes; algorithms for calculating PageRank using a simplified method and damping factor; different PageRank values from actual, toolbars, and ODP; manipulating PageRank ratings; and the relationship between PageRank and the semantic web. It also mentions two similar works from 1998 - Kleinberg's authoritative sources algorithm and Chakrabarti's experiments in topic distillation which added factors to PageRank.
This document summarizes a lecture on graph algorithms and PageRank using MapReduce. It discusses representing graphs in MapReduce, performing breadth-first search, finding shortest paths, and calculating PageRank through an iterative process of redistributing PageRank values along edges in the graph. The PageRank algorithm is broken into phases that map nodes to PageRank fragments, reduce to calculate new PageRank values, and iterate until convergence is reached. While MapReduce has limitations for iterative algorithms, this approach allows processing graph partitions in parallel through multiple MapReduce jobs.
Larry Page and Sergey Brin developed the PageRank algorithm at Stanford University in 1998. PageRank is the backbone of Google's search engine technology. It ranks web pages based on the number and quality of inbound links, with the assumption that more important pages receive more links. The algorithm calculates a probability distribution over web pages to determine each page's importance. It works iteratively until the PageRank values settle close to 1 across all pages. However, PageRank faces issues like preferring older pages and being vulnerable to manipulation through link spamming techniques.
The Google Pagerank algorithm - How does it work?Kundan Bhaduri
This document discusses how Google uses Markov chains and the PageRank algorithm to rank web pages. It begins by explaining Markov chains and how they can model random user behavior on the web. It then describes how Google implemented PageRank as a non-absorbing Markov chain to calculate the probability of a random user reaching any given page. The document outlines issues with applying this to the large-scale web, and proposes techniques like the power method to efficiently approximate PageRank values for the trillion-page internet graph. Finally, it provides an example of how links between related high-authority sites can increase the PageRank of a given page.
The document discusses PageRank and HITS algorithms for web structure mining. It provides an overview of key concepts like hubs, authorities, and link analysis. It then explains PageRank in detail, including how it is calculated iteratively based on the prestige of inbound links. Finally, it provides an example calculation and discusses how additional inbound links can increase a page's PageRank.
The document introduces the PageRank algorithm. It explains that PageRank ranks pages based on the principle that pages with more inlinks are considered more important. It discusses how PageRank addresses issues like cyclic links and pages with no outlinks. It also provides the formula used to calculate PageRank through an iterative process and describes how Google uses over 200 factors beyond PageRank alone to determine search rankings.
This presentation is based on ranking of web pages, mainly it consist of PageRank algorithm and HITS algorithm. It gives brief knowledge of how to calculate page rank by looking at the links between the pages. It tells you about different techniques of search engine optimization.
This presentation describes in simple terms how the PageRank algorithm by Google founders works. It displays the actual algorithm as well as tried to explain how the calculations are done and how ranks are assigned to any webpage.
Algorithmic Web Spam detection - Matt Peters MozConmattthemathman
This document summarizes research into identifying web spam through machine learning algorithms. Key findings include:
- Models using just 32 in-link and on-page features can accurately identify spam 86% of the time. MozTrust, which measures average distance from trusted sites, is a strong predictor of spam.
- Sites with unnatural link profiles like many inbound links from low MozTrust sites or no internal links are at higher risk of being penalized.
- With large datasets, machine learning can moderately easily detect "unnatural" spammy sites and link profiles algorithmically. However, commercial intent is difficult to measure accurately with current data.
A Generalization of the PageRank Algorithm : NOTESSubhajit Sahu
This paper discusses a method of Generalizing PageRank algorithm for different types of networks. Rank of each vertex is considered to be dependent upon both the in- and out-edges. Each edge can also have differing importance. This solves the problem of dead ends and spider traps without the need of taxation (?).
---
Abstract— PageRank is a well-known algorithm that has been used to understand the structure of the Web. In its classical formulation the algorithm considers only forward looking paths in its analysis- a typical web scenario. We propose a generalization of the PageRank algorithm based on both out-links and in-links. This generalization enables the elimination network anomalies- and increases the applicability of the algorithm to an array of new applications in networked data. Through experimental results we illustrate that the proposed generalized PageRank minimizes the effect of network anomalies, and results in more realistic representation of the network.
Keywords- Search Engine; PageRank; Web Structure; Web Mining; Spider-Trap; dead-end; Taxation;Web spamming
Diagnose SEO Issues with Live Search Webmaster ToolsNathan Buggia
The document discusses search engine optimization (SEO) issues and how to diagnose them using Microsoft's Live Search Webmaster Tools. It provides an overview of how search engines work by crawling websites, ranking pages, and displaying search results. Common SEO issues involve pages not being crawled or indexed properly, low rankings, and problems with the search engine results page (SERP). The Live Search Webmaster Tools can help identify and fix issues with crawling, ranking, content, and SERPs.
This document proposes techniques to detect web spam pages by using a small set of manually evaluated "seed" pages. It introduces the concept of a "trust rank" algorithm that assigns scores to pages based on their connectivity to seed pages identified as reputable by human experts. The paper presents an evaluation of these techniques on a large web crawl, finding that a seed set of less than 200 sites can effectively filter out spam from a significant portion of the web.
This document discusses several key aspects of mathematics and algorithms used in internet information retrieval and search engines:
1. It explains how search engines like Google can rapidly rank billions of web pages using algorithms based on the topology and link structure of the web graph, such as PageRank.
2. It describes two main types of page ranking algorithms - static importance ranking based on link analysis, and dynamic relevance ranking based on statistical learning models to match pages to queries.
3. It proposes a new ranking algorithm called BrowseRank that models user browsing behavior using Markov chains and takes into account visit duration to better reflect true page importance.
Seojocktoberfest - link health audits and organic traffic recovery - Scott McLayYard
Scott is one of the UK’s most experienced technical SEOs and adds a strong enterprise level technological focus to Yard’s work. Having worked on technical audits, internationalisation projects and migration support for hundreds of websites, including global household brand names, throughout his 12 year technical SEO career, Scott has industry insights, competitive intelligence and technical solutions readily to hand for Yard clients and he will be sharing some of this knowledge in his presentation on link health audits and organic traffic recovery.
The document discusses several mathematical models and algorithms used in internet information retrieval and search engines:
1. Markov chain methods can be used to model a user's web surfing behavior and page visit transitions.
2. BrowseRank models user browsing as a Markov process to calculate page importance based on observed user behavior rather than artificial assumptions.
3. Learning to rank problems in information retrieval can be framed as a two-layer statistical learning problem where queries are the first layer and document relevance judgments are the second layer.
4. Stability theory can provide generalization bounds for learning to rank algorithms under this two-layer framework. Modifying algorithms like SVM and Boosting to have query-level stability improves performance.
The document lists various tools that can be used to check for broken links on websites, including both free online services and desktop applications for Windows, Linux, and Mac. Some of the tools listed check a single page, while others can check deeper within a site or across an entire website. The tools serve different purposes and have varying capabilities such as link checking, validation of HTML/CSS, spell checking, and visual site mapping.
The document discusses search engine optimization (SEO) and outlines a proposed SEO program. The objectives of SEO are to increase a website's visibility, traffic, and number of visitors. The proposed 6-stage program includes evaluation, on-page optimization, off-page activities like link building, and monitoring rankings and traffic over at least one year. Key deliverables include analysis, recommendations, and reports tracking the program's implementation and results.
This document provides an overview of internet usage in Russia. It finds that Russia has the fastest growing internet population in Europe, with a 27% year-over-year growth rate. The penetration of internet access in Russia lags countries in Western Europe, with only 33% of Russians using the internet. However, among Russian internet users, online shopping and social media activities are highly popular. The largest search engine and websites in Russia are Yandex, Mail.ru, and Rambler. Mobile internet penetration is also growing rapidly in Russia but remains behind other European countries.
This document summarizes the key points from a November 2013 newsletter from Guru99 covering various topics related to search engine optimization and Google updates.
1) Google's Penguin 2.1 spam-fighting algorithm was released, affecting around 1% of searches. Sites impacted may see traffic drops and need to disavow bad links to recover.
2) A new link analysis software, Link Detox Genesis, was introduced with features like unlimited re-processing of reports.
3) A webmaster reported having a manual penalty revoked on the www version of their site by Google, but the penalty remained on the non-www version.
4) An analysis of websites impacted by Penguin 2.0 showed many
1. The document discusses techniques for detecting link-based spam using rank propagation and probabilistic counting.
2. It aims to characterize spam pages and analyze the distribution of statistics about pages to identify outliers associated with spam.
3. The key techniques involve counting the number of "supporters" or pages linking to a target page at different distances from that page.
State of the Art Analysis Approach for Identification of the Malignant URLsIOSRjournaljce
Malicious URLs have been universally used to ascend various cyber attacks including spamming, phishing and malware. Malware, short term for malicious software, is software which is developed to penetrate computers in a network without the user’s permission or notification. Existing methods typically detect malicious URLs of a single attack type. Hence such detection systems are failed to protect the users from various attacks. Malware spreading widely throughout the area of network as consequence of this it becomes predicament in distributed computer and network systems. Malicious links are the place of origin of all attacks which circulated all over the web. Hence malicious URLs should be detected for the prevention of users from these malware attacks. In this paper we described a novel approach which analyze all types of attacks by identifying malicious URLs and secure the web users from them. This technique prevents the users from malignant URLs before visiting them. Therefore efficiency of web security gets maintained. For such anatomization we developed an analyzer which identifies URLs and examine as malicious or benign. We also developed five processes which crawl for suspicious URLs. This approach will prevent the users from all types of attacks and increase efficiency of web crawling phase.
Analyzing the effectivess_and_coverage_of_web_app_scannersLarry Suto
This study statistically evaluated the effectiveness of three leading web application vulnerability scanners (NTOSpider, AppScan, and WebInspect) based on their ability to crawl links, test application coverage, find vulnerabilities, and generate false positives across three test applications of varying complexity. NTOSpider significantly outperformed the other tools, finding over twice as many vulnerabilities as AppScan and over 18 times as many as WebInspect, with zero false positives. AppScan missed 88% of vulnerabilities found by NTOSpider, while WebInspect missed 95%. The study concludes that while AppScan and WebInspect may be suitable for simple applications, organizations should have concerns about relying solely on their results for more complex applications due to the number of
The Google Pagerank algorithm - How does it work?Kundan Bhaduri
This document discusses how Google uses Markov chains and the PageRank algorithm to rank web pages. It begins by explaining Markov chains and how they can model random user behavior on the web. It then describes how Google implemented PageRank as a non-absorbing Markov chain to calculate the probability of a random user reaching any given page. The document outlines issues with applying this to the large-scale web, and proposes techniques like the power method to efficiently approximate PageRank values for the trillion-page internet graph. Finally, it provides an example of how links between related high-authority sites can increase the PageRank of a given page.
The document discusses PageRank and HITS algorithms for web structure mining. It provides an overview of key concepts like hubs, authorities, and link analysis. It then explains PageRank in detail, including how it is calculated iteratively based on the prestige of inbound links. Finally, it provides an example calculation and discusses how additional inbound links can increase a page's PageRank.
The document introduces the PageRank algorithm. It explains that PageRank ranks pages based on the principle that pages with more inlinks are considered more important. It discusses how PageRank addresses issues like cyclic links and pages with no outlinks. It also provides the formula used to calculate PageRank through an iterative process and describes how Google uses over 200 factors beyond PageRank alone to determine search rankings.
This presentation is based on ranking of web pages, mainly it consist of PageRank algorithm and HITS algorithm. It gives brief knowledge of how to calculate page rank by looking at the links between the pages. It tells you about different techniques of search engine optimization.
This presentation describes in simple terms how the PageRank algorithm by Google founders works. It displays the actual algorithm as well as tried to explain how the calculations are done and how ranks are assigned to any webpage.
Algorithmic Web Spam detection - Matt Peters MozConmattthemathman
This document summarizes research into identifying web spam through machine learning algorithms. Key findings include:
- Models using just 32 in-link and on-page features can accurately identify spam 86% of the time. MozTrust, which measures average distance from trusted sites, is a strong predictor of spam.
- Sites with unnatural link profiles like many inbound links from low MozTrust sites or no internal links are at higher risk of being penalized.
- With large datasets, machine learning can moderately easily detect "unnatural" spammy sites and link profiles algorithmically. However, commercial intent is difficult to measure accurately with current data.
A Generalization of the PageRank Algorithm : NOTESSubhajit Sahu
This paper discusses a method of Generalizing PageRank algorithm for different types of networks. Rank of each vertex is considered to be dependent upon both the in- and out-edges. Each edge can also have differing importance. This solves the problem of dead ends and spider traps without the need of taxation (?).
---
Abstract— PageRank is a well-known algorithm that has been used to understand the structure of the Web. In its classical formulation the algorithm considers only forward looking paths in its analysis- a typical web scenario. We propose a generalization of the PageRank algorithm based on both out-links and in-links. This generalization enables the elimination network anomalies- and increases the applicability of the algorithm to an array of new applications in networked data. Through experimental results we illustrate that the proposed generalized PageRank minimizes the effect of network anomalies, and results in more realistic representation of the network.
Keywords- Search Engine; PageRank; Web Structure; Web Mining; Spider-Trap; dead-end; Taxation;Web spamming
Diagnose SEO Issues with Live Search Webmaster ToolsNathan Buggia
The document discusses search engine optimization (SEO) issues and how to diagnose them using Microsoft's Live Search Webmaster Tools. It provides an overview of how search engines work by crawling websites, ranking pages, and displaying search results. Common SEO issues involve pages not being crawled or indexed properly, low rankings, and problems with the search engine results page (SERP). The Live Search Webmaster Tools can help identify and fix issues with crawling, ranking, content, and SERPs.
This document proposes techniques to detect web spam pages by using a small set of manually evaluated "seed" pages. It introduces the concept of a "trust rank" algorithm that assigns scores to pages based on their connectivity to seed pages identified as reputable by human experts. The paper presents an evaluation of these techniques on a large web crawl, finding that a seed set of less than 200 sites can effectively filter out spam from a significant portion of the web.
This document discusses several key aspects of mathematics and algorithms used in internet information retrieval and search engines:
1. It explains how search engines like Google can rapidly rank billions of web pages using algorithms based on the topology and link structure of the web graph, such as PageRank.
2. It describes two main types of page ranking algorithms - static importance ranking based on link analysis, and dynamic relevance ranking based on statistical learning models to match pages to queries.
3. It proposes a new ranking algorithm called BrowseRank that models user browsing behavior using Markov chains and takes into account visit duration to better reflect true page importance.
Seojocktoberfest - link health audits and organic traffic recovery - Scott McLayYard
Scott is one of the UK’s most experienced technical SEOs and adds a strong enterprise level technological focus to Yard’s work. Having worked on technical audits, internationalisation projects and migration support for hundreds of websites, including global household brand names, throughout his 12 year technical SEO career, Scott has industry insights, competitive intelligence and technical solutions readily to hand for Yard clients and he will be sharing some of this knowledge in his presentation on link health audits and organic traffic recovery.
The document discusses several mathematical models and algorithms used in internet information retrieval and search engines:
1. Markov chain methods can be used to model a user's web surfing behavior and page visit transitions.
2. BrowseRank models user browsing as a Markov process to calculate page importance based on observed user behavior rather than artificial assumptions.
3. Learning to rank problems in information retrieval can be framed as a two-layer statistical learning problem where queries are the first layer and document relevance judgments are the second layer.
4. Stability theory can provide generalization bounds for learning to rank algorithms under this two-layer framework. Modifying algorithms like SVM and Boosting to have query-level stability improves performance.
The document lists various tools that can be used to check for broken links on websites, including both free online services and desktop applications for Windows, Linux, and Mac. Some of the tools listed check a single page, while others can check deeper within a site or across an entire website. The tools serve different purposes and have varying capabilities such as link checking, validation of HTML/CSS, spell checking, and visual site mapping.
The document discusses search engine optimization (SEO) and outlines a proposed SEO program. The objectives of SEO are to increase a website's visibility, traffic, and number of visitors. The proposed 6-stage program includes evaluation, on-page optimization, off-page activities like link building, and monitoring rankings and traffic over at least one year. Key deliverables include analysis, recommendations, and reports tracking the program's implementation and results.
This document provides an overview of internet usage in Russia. It finds that Russia has the fastest growing internet population in Europe, with a 27% year-over-year growth rate. The penetration of internet access in Russia lags countries in Western Europe, with only 33% of Russians using the internet. However, among Russian internet users, online shopping and social media activities are highly popular. The largest search engine and websites in Russia are Yandex, Mail.ru, and Rambler. Mobile internet penetration is also growing rapidly in Russia but remains behind other European countries.
This document summarizes the key points from a November 2013 newsletter from Guru99 covering various topics related to search engine optimization and Google updates.
1) Google's Penguin 2.1 spam-fighting algorithm was released, affecting around 1% of searches. Sites impacted may see traffic drops and need to disavow bad links to recover.
2) A new link analysis software, Link Detox Genesis, was introduced with features like unlimited re-processing of reports.
3) A webmaster reported having a manual penalty revoked on the www version of their site by Google, but the penalty remained on the non-www version.
4) An analysis of websites impacted by Penguin 2.0 showed many
1. The document discusses techniques for detecting link-based spam using rank propagation and probabilistic counting.
2. It aims to characterize spam pages and analyze the distribution of statistics about pages to identify outliers associated with spam.
3. The key techniques involve counting the number of "supporters" or pages linking to a target page at different distances from that page.
State of the Art Analysis Approach for Identification of the Malignant URLsIOSRjournaljce
Malicious URLs have been universally used to ascend various cyber attacks including spamming, phishing and malware. Malware, short term for malicious software, is software which is developed to penetrate computers in a network without the user’s permission or notification. Existing methods typically detect malicious URLs of a single attack type. Hence such detection systems are failed to protect the users from various attacks. Malware spreading widely throughout the area of network as consequence of this it becomes predicament in distributed computer and network systems. Malicious links are the place of origin of all attacks which circulated all over the web. Hence malicious URLs should be detected for the prevention of users from these malware attacks. In this paper we described a novel approach which analyze all types of attacks by identifying malicious URLs and secure the web users from them. This technique prevents the users from malignant URLs before visiting them. Therefore efficiency of web security gets maintained. For such anatomization we developed an analyzer which identifies URLs and examine as malicious or benign. We also developed five processes which crawl for suspicious URLs. This approach will prevent the users from all types of attacks and increase efficiency of web crawling phase.
Analyzing the effectivess_and_coverage_of_web_app_scannersLarry Suto
This study statistically evaluated the effectiveness of three leading web application vulnerability scanners (NTOSpider, AppScan, and WebInspect) based on their ability to crawl links, test application coverage, find vulnerabilities, and generate false positives across three test applications of varying complexity. NTOSpider significantly outperformed the other tools, finding over twice as many vulnerabilities as AppScan and over 18 times as many as WebInspect, with zero false positives. AppScan missed 88% of vulnerabilities found by NTOSpider, while WebInspect missed 95%. The study concludes that while AppScan and WebInspect may be suitable for simple applications, organizations should have concerns about relying solely on their results for more complex applications due to the number of
This document discusses techniques for detecting link farms, which are groups of web pages that link to each other to artificially boost their PageRank scores. It provides background on PageRank and how link farms can manipulate it. The proposed method calculates both PageRank and a new "GapRank" score for pages, and identifies pages as part of a link farm if they have identical PageRank and GapRank values. The method is demonstrated on a sample dataset, where pages with duplicate PageRank scores are found and shown to also have identical GapRank, identifying them as a link farm that is then removed from the dataset. This improves the PageRank algorithm's ability to rank pages accurately.
This document discusses techniques for detecting link farms, which are groups of web pages that link to each other to artificially boost their PageRank scores. It provides background on PageRank and how link farms can manipulate it. The proposed method calculates both PageRank and a new "GapRank" score for pages, and identifies pages as part of a link farm if they have identical PageRank and GapRank values. The method is demonstrated on a sample dataset, where pages with duplicate PageRank scores are found and shown to also have identical GapRank, identifying them as a link farm that is then removed from the dataset. This improves the PageRank algorithm's ability to rank pages accurately.
The document discusses authority building (gaining endorsements and mentions from influential sources) versus link building and their impact on search engine rankings. It provides a case study of how gaining shares and mentions from influencers significantly increased traffic to a website. The document also discusses various factors related to authoritative links that can impact rankings, such as quantity, quality, diversity, and relevance of links; and examines the correlations between these factors and search performance.
This document discusses the risks of web scraping for real estate property portals. It notes that web scraping, while sometimes legitimate, can also be used maliciously to steal intellectual property and gain competitive advantages. The real estate industry saw a 300% increase in bad bot traffic in 2015. Web scrapers can replicate real estate portal data and platforms for a low cost, hurting the revenues and SEO rankings of legitimate portals. The document promotes the services of Distil Networks, which provides bot detection and blocking solutions to enhance data and clean up traffic from malicious bots.
Socia Media and Digital Volunteering in Disaster Management @ DSEM 2017Carlos Castillo (ChaTo)
This document discusses using social media and digital volunteering in disaster management. It outlines how crowdsourcing can be used to extract insights from social media data during disasters through tasks like event detection, content labeling, and quality assessment. However, it notes challenges like biases in the data. The document proposes moving beyond individual insights to develop a "big picture" understanding of disasters. It also suggests moving beyond basic crowd processing to more advanced participatory mining with volunteers. Combining authoritative data with social media and integrating human and machine intelligence are presented as promising approaches.
Keynote at the Dutch-Belgian Information Retrieval Workshop, November 2016, Delft, Netherlands.
Based on KDD 2016 tutorial with Sara Hajian and Francesco Bonchi.
The document discusses algorithmic bias and fairness in data mining. It is divided into four parts: 1) discrimination discovery, 2) fairness-aware data mining, 3) challenges and future directions, and 4) discussion. It also covers introduction and context, sources of bias, legal concepts, measures of discrimination, specific contexts like labor markets, and the relationship between privacy and discrimination.
KDD 2016 tutorial on Algorithmic Bias, Parts III and IV.
Video: https://www.youtube.com/watch?v=ErgHjxJsEKA
By Sara Hajian, Francesco Bonchi, and Carlos Castillo.
http://francescobonchi.com/algorithmic_bias_tutorial.html
The document describes different study designs for observational studies, including matching designs. It provides two examples of matching designs used to study the effects of hurricanes on online friendships and the effects of exercise on mental health using Twitter data. The hurricane study matched universities affected by a hurricane with unaffected universities on variables like size and ranking. The exercise study matched Twitter users who tweeted about exercising with similar users who did not exercise. The document also discusses using propensity score matching and difference-in-differences to study the effect of having an answer accepted on question answering sites like Stack Overflow.
Basic concepts about natural experiments, based mostly on Dunning's book.
Lecture for the M. Sc. Data Science, Sapienza University of Rome, Spring 2016.
Predictions of links in graphs based on content and information propagations.
Lecture for the M. Sc. Data Science, Sapienza University of Rome, Spring 2016.
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Link Analysis (RBY)
1. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Link Analysis on the Web
Functional
Rankings
The big picture, the small picture and the medium-sized picture
Web Spam
Web Spam
Detection
Ricardo Baeza-Yates3,4
Topological Web
Spam
Joint work with: L. Becchetti1 , P. Boldi2 , C. Castillo1,3 ,
Direct Counting
D. Donato1,3 , S. Leonardi1 , B. Poblete5
of Supporters
Spam Detection
Results
1. Universit` di Roma “La Sapienza” – Rome, Italy
a
2. Univerit` degli Studi di Milano – Milan, Italy
a
3. Yahoo! Research Barcelona – Catalunya, Spain
4. Yahoo! Research Latin America – Santiago, Chile
5. Universitat Pompeu Fabra – Catalunya, Spain
2. Link Analysis on
the Web
Levels of Link Analysis
1
Levels of Link
Analysis
Generalizing PageRank
2
Generalizing
PageRank
Other
Other Functional Rankings
3
Functional
Rankings
Web Spam
Web Spam
4
Web Spam
Detection
Web Spam Detection
Topological Web 5
Spam
Direct Counting
of Supporters
Topological Web Spam
6
Spam Detection
Results
Direct Counting of Supporters
7
Spam Detection Results
8
3. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Levels of Link Analysis
1
Other
Generalizing PageRank
2
Functional
Other Functional Rankings
Rankings 3
Web Spam
4
Web Spam
Web Spam Detection
5
Web Spam
Detection
Topological Web Spam
6
Topological Web
Direct Counting of Supporters
7
Spam
Spam Detection Results
8
Direct Counting
of Supporters
Spam Detection
Results
4. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
5. Link Analysis on
How to find meaningful patterns?
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Several levels of analysis:
Web Spam
Web Spam
Macroscopic view: overall structure
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
6. Link Analysis on
How to find meaningful patterns?
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Several levels of analysis:
Web Spam
Web Spam
Macroscopic view: overall structure
Detection
Microscopic view: nodes
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
7. Link Analysis on
How to find meaningful patterns?
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Several levels of analysis:
Web Spam
Web Spam
Macroscopic view: overall structure
Detection
Microscopic view: nodes
Topological Web
Spam
Mesoscopic view: regions
Direct Counting
of Supporters
Spam Detection
Results
8. Link Analysis on
Macroscopic view, e.g. Bow-tie
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
[Broder et al., 2000]
9. Link Analysis on
Macroscopic view, e.g. Bow-tie, migration
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
[Baeza-Yates and Poblete, 2006]
10. Link Analysis on
Macroscopic view, e.g. Jellyfish
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
[Tauro et al., 2001] - Internet Autonomous Systems (AS)
Topology
11. Link Analysis on
Macroscopic view, e.g. Jellyfish
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
12. Link Analysis on
Microscopic view, e.g. Degree
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
[Barab´si, 2002] and others
a
13. Link Analysis on
Microscopic view, e.g. Degree
the Web
Greece Chile
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Spain Korea
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
[Baeza-Yates et al., 2006b] - compares this distribution in 8
countries . . . guess what is the result?
14. Link Analysis on
Mesoscopic view, e.g. Hop-plot
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
15. Link Analysis on
Mesoscopic view, e.g. Hop-plot
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
16. Link Analysis on
Mesoscopic view, e.g. Hop-plot
the Web
Levels of Link
Analysis
.it (40M pages) .uk (18M pages)
Generalizing 0.3 0.3
PageRank
Other 0.2 0.2
Frequency
Frequency
Functional
Rankings
0.1 0.1
Web Spam
Web Spam 0.0 0.0
5 10 15 20 25 30 5 10 15 20 25 30
Detection
Distance Distance
Topological Web
.eu.int (800K pages) Synthetic graph (100K pages)
Spam
Direct Counting 0.3 0.3
of Supporters
Spam Detection 0.2 0.2
Frequency
Frequency
Results
0.1 0.1
0.0 0.0
5 10 15 20 25 30 5 10 15 20 25 30
Distance Distance
[Baeza-Yates et al., 2006a]
17. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
18. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Levels of Link Analysis
1
Other
Generalizing PageRank
2
Functional
Other Functional Rankings
Rankings 3
Web Spam
4
Web Spam
Web Spam Detection
5
Web Spam
Detection
Topological Web Spam
6
Topological Web
Direct Counting of Supporters
7
Spam
Spam Detection Results
8
Direct Counting
of Supporters
Spam Detection
Results
19. Link Analysis on
Notation
the Web
Levels of Link
Analysis
Generalizing
Let PN×N be the normalized link matrix of a graph
PageRank
Row-normalized
Other
Functional
Rankings
No “sinks”
Web Spam
Definition (PageRank)
Web Spam
Detection
Stationary state of:
Topological Web
Spam
(1 − α)
Direct Counting
αP + 1N×N
of Supporters
N
Spam Detection
Results
20. Link Analysis on
Notation
the Web
Levels of Link
Analysis
Generalizing
Let PN×N be the normalized link matrix of a graph
PageRank
Row-normalized
Other
Functional
Rankings
No “sinks”
Web Spam
Definition (PageRank)
Web Spam
Detection
Stationary state of:
Topological Web
Spam
(1 − α)
Direct Counting
αP + 1N×N
of Supporters
N
Spam Detection
Results
Follow links with probability α
Random jump with probability 1 − α
21. Link Analysis on
Explicit Formulas
the Web
Levels of Link
Analysis
Generalizing
PageRank
Formulas for PageRank
Other
Functional
[Newman et al., 2001, Boldi et al., 2005]
Rankings
Web Spam
∞
(1 − α)
Web Spam
(αP)t .
r(α) =
Detection
N
t=0
Topological Web
Spam
(1 − α)α|p|
Direct Counting
ri (α) = branching(p)
of Supporters
N
Spam Detection p∈Path(−,i)
Results
22. Link Analysis on
Explicit Formulas
the Web
Levels of Link
Analysis
Generalizing
PageRank
Formulas for PageRank
Other
Functional
[Newman et al., 2001, Boldi et al., 2005]
Rankings
Web Spam
∞
(1 − α)
Web Spam
(αP)t .
r(α) =
Detection
N
t=0
Topological Web
Spam
(1 − α)α|p|
Direct Counting
ri (α) = branching(p)
of Supporters
N
Spam Detection p∈Path(−,i)
Results
Path(−, i) are incoming paths in node i
23. Link Analysis on
Branching contribution
the Web
Levels of Link
Analysis
Generalizing
PageRank
Definition (Branching contribution of a path)
Other
Functional
Given a path p = x1 , x2 , . . . , xt of length t = |p|
Rankings
Web Spam
1
branching(p) =
Web Spam
d1 d2 · · · dt−1
Detection
Topological Web
where di are the out-degrees of the members of the path
Spam
Direct Counting
For every node i and every length t
of Supporters
Spam Detection
Results
branching(p) = 1.
p∈Path(i,−),|p|=t
24. Link Analysis on
Functional ranking
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
General functional ranking [Baeza-Yates et al., 2006a]
Web Spam
Web Spam
damping(|p|)
Detection
ri (α) = branching(p)
N
Topological Web
p∈Path(−,i)
Spam
Direct Counting
PageRank is a particular case of path-based ranking
of Supporters
Spam Detection
Results
25. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Levels of Link Analysis
1
Other
Generalizing PageRank
2
Functional
Other Functional Rankings
Rankings 3
Web Spam
4
Web Spam
Web Spam Detection
5
Web Spam
Detection
Topological Web Spam
6
Topological Web
Direct Counting of Supporters
7
Spam
Spam Detection Results
8
Direct Counting
of Supporters
Spam Detection
Results
26. Link Analysis on
Exponential damping = PageRank
the Web
Levels of Link
0.30
Analysis
damping(t) with α=0.8
damping(t) with α=0.7
Generalizing
PageRank
Other
0.20
Functional
Weight
Rankings
Web Spam
Web Spam
0.10
Detection
Topological Web
Spam
Direct Counting
0.00
of Supporters
1 2 345678 9 10
Spam Detection
Length of the path (t)
Results
Exponential damping = PageRank
damping(t) = α(1 − α)t
Most of the contribution is on the first few levels.
27. Link Analysis on
Linear damping
the Web
0.30
Levels of Link
damping(t) with L=15
Analysis
damping(t) with L=10
Generalizing
PageRank
0.20
Other
Functional
Weight
Rankings
Web Spam
0.10
Web Spam
Detection
Topological Web
Spam
0.00
Direct Counting
of Supporters
1 2 345678 9 10
Spam Detection
Length of the path (t)
Results
Linear damping
2(L−t)
t<L
L(L+1)
damping(t) =
t≥L
0
28. Link Analysis on
Example: Calculating LinearRank
the Web
Levels of Link
Analysis
Generalizing
PageRank
For calculating LinearRank we use:
Other
Functional
Rankings
∞
1
Web Spam
damping(t)Pt
LinearRank =
N
Web Spam
t=0
Detection
L−1
Topological Web
2(L − t) t
1
Spam
= P
N L(L + 1)
Direct Counting
t=0
of Supporters
Spam Detection
Results
29. Link Analysis on
Example: Calculating LinearRank
the Web
Levels of Link
Analysis
Generalizing
PageRank
For calculating LinearRank we use:
Other
Functional
Rankings
∞
1
Web Spam
damping(t)Pt
LinearRank =
N
Web Spam
t=0
Detection
L−1
Topological Web
2(L − t) t
1
Spam
= P
N L(L + 1)
Direct Counting
t=0
of Supporters
Spam Detection
Results
However, we cannot hold the temporary Pt in memory!
30. Link Analysis on
Re-write the damping as a recursion
the Web
Levels of Link
Analysis
Generalizing
PageRank
We have to rewrite to be able to calculate:
Other
Functional
2
Rankings
R(0) =
Web Spam
L+1
Web Spam
(L − k − 1) (k)
Detection
R(k+1) = RP
(L − k)
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
31. Link Analysis on
Re-write the damping as a recursion
the Web
Levels of Link
Analysis
Generalizing
PageRank
We have to rewrite to be able to calculate:
Other
Functional
2
Rankings
R(0) =
Web Spam
L+1
Web Spam
(L − k − 1) (k)
Detection
R(k+1) = RP
(L − k)
Topological Web
Spam
L−1
Direct Counting
R(k)
LinearRank =
of Supporters
Spam Detection k=0
Results
32. Link Analysis on
Re-write the damping as a recursion
the Web
Levels of Link
Analysis
Generalizing
PageRank
We have to rewrite to be able to calculate:
Other
Functional
2
Rankings
R(0) =
Web Spam
L+1
Web Spam
(L − k − 1) (k)
Detection
R(k+1) = RP
(L − k)
Topological Web
Spam
L−1
Direct Counting
R(k)
LinearRank =
of Supporters
Spam Detection k=0
Results
Now we can give the algorithm . . .
33. Link Analysis on
Algorithm
the Web
Levels of Link
for i : 1 . . . N do {Initialization}
1:
Analysis
2
Score[i] ← R[i] ← L+1
2:
Generalizing
PageRank
3: end for
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
34. Link Analysis on
Algorithm
the Web
Levels of Link
for i : 1 . . . N do {Initialization}
1:
Analysis
2
Score[i] ← R[i] ← L+1
2:
Generalizing
PageRank
end for
3:
Other
for k : 1 . . . L − 1 do {Iteration step}
4:
Functional
Rankings
Aux ← 0
5:
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
35. Link Analysis on
Algorithm
the Web
Levels of Link
for i : 1 . . . N do {Initialization}
1:
Analysis
2
Score[i] ← R[i] ← L+1
2:
Generalizing
PageRank
end for
3:
Other
for k : 1 . . . L − 1 do {Iteration step}
4:
Functional
Rankings
Aux ← 0
5:
Web Spam
for i : 1 . . . N do {Follow links in the graph}
6:
Web Spam
for all j such that there is a link from i to j do
7:
Detection
Aux[j] ← Aux[j] + R[i]/outdegree(i)
Topological Web 8:
Spam
end for
9:
Direct Counting
end for
of Supporters 10:
Spam Detection
Results
36. Link Analysis on
Algorithm
the Web
Levels of Link
for i : 1 . . . N do {Initialization}
1:
Analysis
2
Score[i] ← R[i] ← L+1
2:
Generalizing
PageRank
end for
3:
Other
for k : 1 . . . L − 1 do {Iteration step}
4:
Functional
Rankings
Aux ← 0
5:
Web Spam
for i : 1 . . . N do {Follow links in the graph}
6:
Web Spam
for all j such that there is a link from i to j do
7:
Detection
Aux[j] ← Aux[j] + R[i]/outdegree(i)
Topological Web 8:
Spam
end for
9:
Direct Counting
end for
of Supporters 10:
for i : 1 . . . N do {Add to ranking value}
Spam Detection
11:
Results
R[i] ← Aux[i] × (L−k−1)
12: (L−k)
Score[i] ← Score[i] + R[i]
13:
end for
14:
end for
15:
return Score
16:
37. Link Analysis on
Algorithm (general)
the Web
Levels of Link
for i : 1 . . . N do {Initialization}
1:
Analysis
Score[i] ← R[i] ← INIT
2:
Generalizing
PageRank
end for
3:
Other
for k : 1 . . . STOP do {Iteration step}
4:
Functional
Rankings
Aux ← 0
5:
Web Spam
for i : 1 . . . N do {Follow links in the graph}
6:
Web Spam
for all j such that there is a link from i to j do
Detection 7:
Aux[j] ← Aux[j] + R[i]/outdegree(i)
Topological Web
8:
Spam
end for
9:
Direct Counting
of Supporters
end for
10:
Spam Detection
for i : 1 . . . N do {Add to ranking value}
11:
Results
R[i] ← Aux[i] × FACTOR
12:
Score[i] ← Score[i] + R[i]
13:
end for
14:
end for
15:
return Score
16:
38. Link Analysis on
Other damping functions
the Web
Levels of Link
Analysis
Empirical damping:
Generalizing
PageRank
0.7
Other
Functional
Rankings
Average text similarity 0.6
Web Spam
Web Spam
0.5
Detection
Topological Web
Spam
0.4
Direct Counting
of Supporters
0.3
Spam Detection
Results
0.2
1 2 3 4 5
Link distance
39. Link Analysis on
Using LinearRank to approximage PageRank
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Experimental comparison: 18-million nodes in the U.K. Web
Rankings
Web Spam
Graph
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
40. Link Analysis on
Using LinearRank to approximage PageRank
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Experimental comparison: 18-million nodes in the U.K. Web
Rankings
Web Spam
Graph
Web Spam
Calculated PageRank with α = 0.1, 0.2, . . . , 0.9
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
41. Link Analysis on
Using LinearRank to approximage PageRank
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Experimental comparison: 18-million nodes in the U.K. Web
Rankings
Web Spam
Graph
Web Spam
Calculated PageRank with α = 0.1, 0.2, . . . , 0.9
Detection
Topological Web
Calculated LinearRank with L = 5, 10, . . . , 25
Spam
Direct Counting
of Supporters
Spam Detection
Results
42. Link Analysis on
Using LinearRank to approximage PageRank
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Experimental comparison: 18-million nodes in the U.K. Web
Rankings
Web Spam
Graph
Web Spam
Calculated PageRank with α = 0.1, 0.2, . . . , 0.9
Detection
Topological Web
Calculated LinearRank with L = 5, 10, . . . , 25
Spam
For certain combinations of parameters, the rankings are
Direct Counting
of Supporters
almost equal!
Spam Detection
Results
43. Link Analysis on
Experimental comparison
the Web
Levels of Link
Analysis
Experimental Comparison in the U.K. Web Graph
Generalizing
PageRank
Other
Functional
1.00
Rankings
0.95
Web Spam
τ
0.90
Web Spam
Detection
0.85
τ ≥ 0.95
Topological Web
0.80
Spam
Direct Counting
of Supporters
25
Spam Detection
20
Results
0.9
15
L 0.8
10 0.7
α
0.6
5 0.5
44. Link Analysis on
Prediction of best parameter combination
the Web
Levels of Link
Analysis
Prediction of Best Parameter Combinations (Analysis)
Generalizing
PageRank
25
Actual optimum
Other
Predicted optimum with length=5
Functional
Rankings
L that maximizes Kendall’s τ
20
Web Spam
Web Spam
Detection
15
Topological Web
Spam
10
Direct Counting
of Supporters
Spam Detection
Results
5
0.5 0.6 0.7 0.8 0.9
Exponent α
45. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Levels of Link Analysis
1
Other
Generalizing PageRank
2
Functional
Other Functional Rankings
Rankings 3
Web Spam
4
Web Spam
Web Spam Detection
5
Web Spam
Detection
Topological Web Spam
6
Topological Web
Direct Counting of Supporters
7
Spam
Spam Detection Results
8
Direct Counting
of Supporters
Spam Detection
Results
46. Link Analysis on
What is on the Web?
the Web
Information
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
47. Link Analysis on
What is on the Web?
the Web
Information + Porn
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
48. Link Analysis on
What is on the Web?
the Web
Information + Porn + On-line casinos + Free movies +
Levels of Link
Analysis
Cheap software + Buy a MBA diploma + Prescription -free
Generalizing
drugs + V!-4-gra + Get rich now now now!!!
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
Graphic: www.milliondollarhomepage.com
49. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
50. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Keyword stuffing
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
51. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Keyword stuffing
Functional
Rankings
Link farms
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
52. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Keyword stuffing
Functional
Rankings
Link farms
Web Spam
Scraper, “Made for Advertising” sites
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
53. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Keyword stuffing
Functional
Rankings
Link farms
Web Spam
Scraper, “Made for Advertising” sites
Web Spam
Spam blogs (splogs)
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
54. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Keyword stuffing
Functional
Rankings
Link farms
Web Spam
Scraper, “Made for Advertising” sites
Web Spam
Spam blogs (splogs)
Detection
Cloaking
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
55. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Keyword stuffing
Functional
Rankings
Link farms
Web Spam
Scraper, “Made for Advertising” sites
Web Spam
Spam blogs (splogs)
Detection
Cloaking
Topological Web
Spam
Click spam
Direct Counting
of Supporters
Spam Detection
Results
56. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Keyword stuffing
Functional
Rankings
Link farms
Web Spam
Scraper, “Made for Advertising” sites
Web Spam
Spam blogs (splogs)
Detection
Cloaking
Topological Web
Spam
Click spam
Direct Counting
of Supporters
Spam Detection
Results
57. Link Analysis on
Opportunities for Web spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
V Spamdexing
Other
Keyword stuffing
Functional
Rankings
Link farms
Web Spam
Scraper, “Made for Advertising” sites
Web Spam
Spam blogs (splogs)
Detection
Cloaking
Topological Web
Spam
Click spam
Direct Counting
of Supporters
Adversarial relationship
Spam Detection
Results
Every undeserved gain in ranking for a spammer, is a loss of
precision for the search engine.
58. Link Analysis on
Typical Web Spam (1)
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
59. Link Analysis on
Typical Web Spam (2)
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
60. Link Analysis on
Hidden text
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
61. Link Analysis on
Made for Advertising (1)
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
62. Link Analysis on
Made for Advertising (2)
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
63. Link Analysis on
Made for Advertising (3)
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
64. Link Analysis on
Search engine?
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
65. Link Analysis on
Fake search engine
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
66. Link Analysis on
Problem: “normal” pages that are spam
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
67. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Levels of Link Analysis
1
Other
Generalizing PageRank
2
Functional
Other Functional Rankings
Rankings 3
Web Spam
4
Web Spam
Web Spam Detection
5
Web Spam
Detection
Topological Web Spam
6
Topological Web
Direct Counting of Supporters
7
Spam
Spam Detection Results
8
Direct Counting
of Supporters
Spam Detection
Results
68. Link Analysis on
Machine Learning
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
69. Link Analysis on
Machine Learning (cont.)
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
70. Link Analysis on
Feature Extraction
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
71. Link Analysis on
Challenges: Machine Learning
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Machine Learning Challenges:
Web Spam
Web Spam
Learning with inter dependent variables (graph)
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
72. Link Analysis on
Challenges: Machine Learning
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Machine Learning Challenges:
Web Spam
Web Spam
Learning with inter dependent variables (graph)
Detection
Learning with few examples
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
73. Link Analysis on
Challenges: Machine Learning
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Machine Learning Challenges:
Web Spam
Web Spam
Learning with inter dependent variables (graph)
Detection
Learning with few examples
Topological Web
Spam
Scalability
Direct Counting
of Supporters
Spam Detection
Results
74. Link Analysis on
Challenges: Information Retrieval
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Information Retrieval Challenges:
Rankings
Feature extraction: which features?
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
75. Link Analysis on
Challenges: Information Retrieval
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Information Retrieval Challenges:
Rankings
Feature extraction: which features?
Web Spam
Web Spam
Feature aggregation: page/host/domain
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
76. Link Analysis on
Challenges: Information Retrieval
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Information Retrieval Challenges:
Rankings
Feature extraction: which features?
Web Spam
Web Spam
Feature aggregation: page/host/domain
Detection
Topological Web
Feature propagation (graph)
Spam
Direct Counting
of Supporters
Spam Detection
Results
77. Link Analysis on
Challenges: Information Retrieval
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Information Retrieval Challenges:
Rankings
Feature extraction: which features?
Web Spam
Web Spam
Feature aggregation: page/host/domain
Detection
Topological Web
Feature propagation (graph)
Spam
Recall/precision tradeoffs
Direct Counting
of Supporters
Spam Detection
Results
78. Link Analysis on
Challenges: Information Retrieval
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Information Retrieval Challenges:
Rankings
Feature extraction: which features?
Web Spam
Web Spam
Feature aggregation: page/host/domain
Detection
Topological Web
Feature propagation (graph)
Spam
Recall/precision tradeoffs
Direct Counting
of Supporters
Scalability
Spam Detection
Results
79. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Levels of Link Analysis
1
Other
Generalizing PageRank
2
Functional
Other Functional Rankings
Rankings 3
Web Spam
4
Web Spam
Web Spam Detection
5
Web Spam
Detection
Topological Web Spam
6
Topological Web
Direct Counting of Supporters
7
Spam
Spam Detection Results
8
Direct Counting
of Supporters
Spam Detection
Results
80. Link Analysis on
Topological spam: link farms
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
81. Link Analysis on
Topological spam: link farms
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
Single-level farms can be detected by searching groups of
nodes sharing their out-links [Gibson et al., 2005]
82. Link Analysis on
Motivation
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
[Fetterly et al., 2004] hypothesized that studying the
Rankings
distribution of statistics about pages could be a good way of
Web Spam
Web Spam
detecting spam pages:
Detection
Topological Web
“in a number of these distributions, outlier values are
Spam
Direct Counting
associated with web spam”
of Supporters
Spam Detection
Results
83. Link Analysis on
Test collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
U.K. collection
Other
Functional
Rankings
18.5 million pages downloaded from the .UK domain
Web Spam
5,344 hosts manually classified (6% of the hosts)
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
84. Link Analysis on
Test collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
U.K. collection
Other
Functional
Rankings
18.5 million pages downloaded from the .UK domain
Web Spam
5,344 hosts manually classified (6% of the hosts)
Web Spam
Detection
Topological Web
Spam
Direct Counting
Classified entire hosts:
of Supporters
Spam Detection
V A few hosts are mixed: spam and non-spam pages
Results
X More coverage: sample covers 32% of the pages
85. Link Analysis on
In-degree
the Web
δ = 0.35
In−degree
Levels of Link
Analysis
Generalizing
Normal
PageRank
0.4 Spam
Other
Functional
Rankings
0.3
Web Spam
Web Spam
Detection
Topological Web
0.2
Spam
Direct Counting
of Supporters
Spam Detection
0.1
Results
0
1 100 10000
Number of in−links
(δ = max. difference in C.D.F. plot)
86. Link Analysis on
Out-degree
the Web
Levels of Link
δ = 0.28
Out−degree
Analysis
0.3
Generalizing
Normal
PageRank
Spam
Other
Functional
Rankings
Web Spam
0.2
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
0.1
Spam Detection
Results
0
1 10 50 100
Number of out−links
87. Link Analysis on
Edge reciprocity
the Web
Levels of Link
δ = 0.35
Reciprocity of max. PR page
Analysis
0.5
Generalizing
Normal
PageRank
Spam
Other
Functional
0.4
Rankings
Web Spam
Web Spam
0.3
Detection
Topological Web
Spam
0.2
Direct Counting
of Supporters
Spam Detection
Results
0.1
0
0 0.2 0.4 0.6 0.8 1
Fraction of reciprocal links
88. Link Analysis on
Assortativity
the Web
Levels of Link
δ = 0.31
Degree / Degree of neighbors
Analysis
Generalizing
0.4
PageRank
Normal
Spam
Other
Functional
Rankings
0.3
Web Spam
Web Spam
Detection
Topological Web
0.2
Spam
Direct Counting
of Supporters
Spam Detection
0.1
Results
0
0.001 0.01 0.1 1 10 100 1000
Degree/Degree ratio of home page
89. Link Analysis on
Variance of PageRank
the Web
Suggested in [Bencz´r et al., 2005]
u
Levels of Link
Analysis
Generalizing
PageRank
PageRank PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
90. Link Analysis on
Variance of PageRank of in-neighbors
the Web
Levels of Link
Stdev. of PR of Neighbors (Home) δ = 0.41
Analysis
Generalizing
PageRank
Normal
Spam
Other
0.3
Functional
Rankings
Web Spam
Web Spam
Detection
0.2
Topological Web
Spam
Direct Counting
of Supporters
0.1
Spam Detection
Results
0
0 0.2 0.4 0.6 0.8 1
σ2 of the logarithm of PageRank
91. Link Analysis on
TrustRank
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
TrustRank [Gy¨ngyi et al., 2004]
o
Functional
Rankings
A node with high PageRank, but far away from a core set of
Web Spam
“trusted nodes” is suspicious
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
92. Link Analysis on
TrustRank
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
TrustRank [Gy¨ngyi et al., 2004]
o
Functional
Rankings
A node with high PageRank, but far away from a core set of
Web Spam
“trusted nodes” is suspicious
Web Spam
Detection
Start from a set of trusted nodes, then do a random walk,
Topological Web
Spam
returning to the set of trusted nodes with probability 1 − α at
Direct Counting
each step
of Supporters
Spam Detection
Results
i Trusted nodes: data from http://www.dmoz.org/
93. Link Analysis on
TrustRank Idea
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
94. Link Analysis on
TrustRank score
the Web
Levels of Link
δ = 0.59
Analysis
TrustRank score of home page
Generalizing
PageRank
Normal
0.4 Spam
Other
Functional
Rankings
Web Spam
0.3
Web Spam
Detection
Topological Web
Spam
0.2
Direct Counting
of Supporters
Spam Detection
0.1
Results
0
1e−06 0.001
TrustRank
95. Link Analysis on
TrustRank / PageRank
the Web
Levels of Link
δ = 0.59
Analysis
Estimated relative non−spam mass
Generalizing
PageRank
Normal
0.8
Spam
Other
Functional
0.7
Rankings
Web Spam
0.6
Web Spam
0.5
Detection
Topological Web
0.4
Spam
Direct Counting
0.3
of Supporters
Spam Detection
0.2
Results
0.1
0
0.3 1 10 100
TrustRank score/PageRank
96. Link Analysis on
Truncated PageRank
the Web
Levels of Link
Analysis
Generalizing
Proposed in [Becchetti et al., 2006b]. Idea: reduce the direct
PageRank
contribution of the first levels of links:
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
t≤T
0
Results
damping(t) =
C αt t>T
97. Link Analysis on
Truncated PageRank
the Web
Levels of Link
Analysis
Generalizing
Proposed in [Becchetti et al., 2006b]. Idea: reduce the direct
PageRank
contribution of the first levels of links:
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
t≤T
0
Results
damping(t) =
C αt t>T
V No extra reading of the graph after PageRank
98. Link Analysis on
Truncated PageRank(T=2) / PageRank
the Web
Levels of Link
Analysis
TruncatedPageRank T=2 / PageRank δ = 0.30
Generalizing
PageRank
Normal
Other
Spam
0.3
Functional
Rankings
Web Spam
Web Spam
Detection
0.2
Topological Web
Spam
Direct Counting
of Supporters
0.1
Spam Detection
Results
0
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
TruncatedPageRank(T=2) / PageRank
99. Link Analysis on
Max. change of Truncated PageRank
the Web
Levels of Link
Analysis
Maximum change of Truncated PageRank δ = 0.29
Generalizing
PageRank
Normal
Other
Spam
Functional
Rankings
0.2
Web Spam
Web Spam
Detection
Topological Web
Spam
0.1
Direct Counting
of Supporters
Spam Detection
Results
0
0.85 0.9 0.95 1 1.05 1.1
max(TrPRi+1/TrPri)
100. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Levels of Link Analysis
1
Other
Generalizing PageRank
2
Functional
Other Functional Rankings
Rankings 3
Web Spam
4
Web Spam
Web Spam Detection
5
Web Spam
Detection
Topological Web Spam
6
Topological Web
Direct Counting of Supporters
7
Spam
Spam Detection Results
8
Direct Counting
of Supporters
Spam Detection
Results
101. Link Analysis on
High and low-ranked pages are different
the Web
4
Levels of Link
x 10
Analysis
Top 0%−10%
12
Generalizing
Top 40%−50%
PageRank
Top 60%−70%
Other
10
Number of Nodes
Functional
Rankings
8
Web Spam
Web Spam
Detection
6
Topological Web
Spam
4
Direct Counting
of Supporters
2
Spam Detection
Results
0
1 5 10 15 20
Distance
102. Link Analysis on
High and low-ranked pages are different
the Web
4
Levels of Link
x 10
Analysis
Top 0%−10%
12
Generalizing
Top 40%−50%
PageRank
Top 60%−70%
Other
10
Number of Nodes
Functional
Rankings
8
Web Spam
Web Spam
Detection
6
Topological Web
Spam
4
Direct Counting
of Supporters
2
Spam Detection
Results
0
1 5 10 15 20
Distance
Areas below the curves are equal if we are in the same
strongly-connected component
103. Link Analysis on
Probabilistic counting
the Web
Levels of Link
Analysis
1
1
Generalizing 0
0
PageRank 0
0
0
0
Other 0 1
1 1
1
1
Functional 0 0
1 1
0
0
Rankings 0
0 0 0
Propagation of 0
0 1
1
Web Spam bits using the 1
0 1
1
“OR” operation 1
0 1
0
Web Spam
Detection
1
Target
0 Count bits set
Topological Web 0
page
0 to estimate
Spam 0
0 supporters
0
0
Direct Counting 1
1 1
1
of Supporters 0
0 1
1
0
0
Spam Detection 0
0
Results 1
1
0
0
104. Link Analysis on
Probabilistic counting
the Web
Levels of Link
Analysis
1
1
Generalizing 0
0
PageRank 0
0
0
0
Other 0 1
1 1
1
1
Functional 0 0
1 1
0
0
Rankings 0
0 0 0
Propagation of 0
0 1
1
Web Spam bits using the 1
0 1
1
“OR” operation 1
0 1
0
Web Spam
Detection
1
Target
0 Count bits set
Topological Web 0
page
0 to estimate
Spam 0
0 supporters
0
0
Direct Counting 1
1 1
1
of Supporters 0
0 1
1
0
0
Spam Detection 0
0
Results 1
1
0
0
[Becchetti et al., 2006b] shows an improvement of ANF
algorithm [Palmer et al., 2002] based on probabilistic
counting [Flajolet and Martin, 1985]
105. Link Analysis on
General algorithm
the Web
Require: N: number of nodes, d: distance, k: bits
Levels of Link
Analysis
1: for node : 1 . . . N, bit: 1 . . . k do
Generalizing
INIT(node,bit)
2:
PageRank
3: end for
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
106. Link Analysis on
General algorithm
the Web
Require: N: number of nodes, d: distance, k: bits
Levels of Link
Analysis
1: for node : 1 . . . N, bit: 1 . . . k do
Generalizing
INIT(node,bit)
2:
PageRank
3: end for
Other
Functional
4: for distance : 1 . . . d do {Iteration step}
Rankings
Aux ← 0k
Web Spam 5:
for src : 1 . . . N do {Follow links in the graph}
Web Spam
6:
Detection
for all links from src to dest do
7:
Topological Web
Aux[dest] ← Aux[dest] OR V[src,·]
Spam
8:
Direct Counting
end for
9:
of Supporters
end for
10:
Spam Detection
Results
V ← Aux
11:
12: end for
107. Link Analysis on
General algorithm
the Web
Require: N: number of nodes, d: distance, k: bits
Levels of Link
Analysis
1: for node : 1 . . . N, bit: 1 . . . k do
Generalizing
INIT(node,bit)
2:
PageRank
3: end for
Other
Functional
4: for distance : 1 . . . d do {Iteration step}
Rankings
Aux ← 0k
Web Spam 5:
for src : 1 . . . N do {Follow links in the graph}
Web Spam
6:
Detection
for all links from src to dest do
7:
Topological Web
Aux[dest] ← Aux[dest] OR V[src,·]
Spam
8:
Direct Counting
end for
9:
of Supporters
end for
10:
Spam Detection
Results
V ← Aux
11:
12: end for
13: for node: 1 . . . N do {Estimate supporters}
Supporters[node] ← ESTIMATE( V[node,·] )
14:
15: end for
16: return Supporters
108. Link Analysis on
Our estimator
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Initialize all bits to one with probability
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
109. Link Analysis on
Our estimator
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Initialize all bits to one with probability
Rankings
ones(node)
Estimator: neighbors(node) = log(1− ) 1 −
Web Spam
k
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
110. Link Analysis on
Our estimator
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Initialize all bits to one with probability
Rankings
ones(node)
Estimator: neighbors(node) = log(1− ) 1 −
Web Spam
k
Web Spam
Detection
Adaptive estimation
Topological Web
Spam
Repeat the above process for = 1/2, 1/4, 1/8, . . . , and look
Direct Counting
for the transitions from more than (1 − 1/e)k ones to less
of Supporters
than (1 − 1/e)k ones.
Spam Detection
Results
111. Link Analysis on
Convergence
the Web
Levels of Link
Analysis
100%
Generalizing
PageRank
90%
Other
80%
Functional
Rankings
Fraction of nodes
70%
with estimates
Web Spam
60%
Web Spam
Detection
50% d=1
Topological Web
d=2
40%
Spam
d=3
Direct Counting
30% d=4
of Supporters
d=5
20%
Spam Detection
d=6
Results
d=7
10%
d=8
0%
5 10 15 20
Iteration
112. Link Analysis on
Error rate
the Web
Levels of Link
Analysis
Generalizing
Ours 64 bits, epsilon−only estimator
PageRank
Ours 64 bits, combined estimator
0.5
Other
ANF 24 bits × 24 iterations (576 b×i)
Average Relative Error
Functional
ANF 24 bits × 48 iterations (1152 b×i)
Rankings
0.4
Web Spam
960 b×i
Web Spam
1216 b×i
512 b×i 832 b×i
Detection 1344 b×i 1408 b×i
768 b×i 1152 b×i
0.3
Topological Web
Spam
0.2
Direct Counting 576 b×i
1152 b×i
of Supporters
512 b×i 768 b×i 960 b×i 1216 b×i 1344 b×i 1408 b×i
832 b×i 1152 b×i
Spam Detection
0.1
Results
0
1 2 3 4 5 6 7 8
Distance
113. Link Analysis on
Hosts at distance 4
the Web
Levels of Link
δ = 0.39
Hosts at Distance Exactly 4
Analysis
0.4
Generalizing
Normal
PageRank
Spam
Other
Functional
Rankings
0.3
Web Spam
Web Spam
Detection
Topological Web
0.2
Spam
Direct Counting
of Supporters
Spam Detection
0.1
Results
0
1 100 1000
S4 − S3
114. Link Analysis on
Minimum change of supporters
the Web
Levels of Link
δ = 0.39
Minimum change of supporters
Analysis
Generalizing
PageRank
Normal
0.4 Spam
Other
Functional
Rankings
Web Spam
0.3
Web Spam
Detection
Topological Web
Spam
0.2
Direct Counting
of Supporters
Spam Detection
0.1
Results
0
1 5 10
min(S2/S1, S3/S2, S4/S3)
115. Link Analysis on
the Web
Levels of Link
Analysis
Generalizing
PageRank
Levels of Link Analysis
1
Other
Generalizing PageRank
2
Functional
Other Functional Rankings
Rankings 3
Web Spam
4
Web Spam
Web Spam Detection
5
Web Spam
Detection
Topological Web Spam
6
Topological Web
Direct Counting of Supporters
7
Spam
Spam Detection Results
8
Direct Counting
of Supporters
Spam Detection
Results
116. Link Analysis on
Detection rates
the Web
Levels of Link
Analysis
Generalizing
PageRank
60% (UK-2006) – 80% (UK-2002) of detection rate, with
Other
Functional
4%–2% error rate by combining different
Rankings
attributes [Becchetti et al., 2006a].
Web Spam
Web Spam
X No magic bullet in link analysis
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
117. Link Analysis on
Detection rates
the Web
Levels of Link
Analysis
Generalizing
PageRank
60% (UK-2006) – 80% (UK-2002) of detection rate, with
Other
Functional
4%–2% error rate by combining different
Rankings
attributes [Becchetti et al., 2006a].
Web Spam
Web Spam
X No magic bullet in link analysis
Detection
X
Topological Web
Precision still low compared to e-mail spam filters
Spam
Direct Counting
of Supporters
Spam Detection
Results
118. Link Analysis on
Detection rates
the Web
Levels of Link
Analysis
Generalizing
PageRank
60% (UK-2006) – 80% (UK-2002) of detection rate, with
Other
Functional
4%–2% error rate by combining different
Rankings
attributes [Becchetti et al., 2006a].
Web Spam
Web Spam
X No magic bullet in link analysis
Detection
X
Topological Web
Precision still low compared to e-mail spam filters
Spam
V Measure both home page and max. PageRank page
Direct Counting
of Supporters
Spam Detection
Results
119. Link Analysis on
Detection rates
the Web
Levels of Link
Analysis
Generalizing
PageRank
60% (UK-2006) – 80% (UK-2002) of detection rate, with
Other
Functional
4%–2% error rate by combining different
Rankings
attributes [Becchetti et al., 2006a].
Web Spam
Web Spam
X No magic bullet in link analysis
Detection
X
Topological Web
Precision still low compared to e-mail spam filters
Spam
V Measure both home page and max. PageRank page
Direct Counting
of Supporters
V Host-based counts of neighbors are important
Spam Detection
Results
120. Link Analysis on
Detection rates
the Web
Levels of Link
Analysis
Generalizing
PageRank
60% (UK-2006) – 80% (UK-2002) of detection rate, with
Other
Functional
4%–2% error rate by combining different
Rankings
attributes [Becchetti et al., 2006a].
Web Spam
Web Spam
X No magic bullet in link analysis
Detection
X
Topological Web
Precision still low compared to e-mail spam filters
Spam
V Measure both home page and max. PageRank page
Direct Counting
of Supporters
V Host-based counts of neighbors are important
Spam Detection
Results
121. Link Analysis on
Detection rates
the Web
Levels of Link
Analysis
Generalizing
PageRank
60% (UK-2006) – 80% (UK-2002) of detection rate, with
Other
Functional
4%–2% error rate by combining different
Rankings
attributes [Becchetti et al., 2006a].
Web Spam
Web Spam
X No magic bullet in link analysis
Detection
X
Topological Web
Precision still low compared to e-mail spam filters
Spam
V Measure both home page and max. PageRank page
Direct Counting
of Supporters
V Host-based counts of neighbors are important
Spam Detection
Results
Next step: combine link analysis and content analysis
122. Link Analysis on
Upcoming Web Spam Challenge on UK-2006
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
We asked 20+ volunteers to clasify entire hosts
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
123. Link Analysis on
Upcoming Web Spam Challenge on UK-2006
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
We asked 20+ volunteers to clasify entire hosts
Web Spam
Web Spam
We provided several examples
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
124. Link Analysis on
Upcoming Web Spam Challenge on UK-2006
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
We asked 20+ volunteers to clasify entire hosts
Web Spam
Web Spam
We provided several examples
Detection
Asked to classify normal / borderline / spam
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
125. Link Analysis on
Upcoming Web Spam Challenge on UK-2006
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
We asked 20+ volunteers to clasify entire hosts
Web Spam
Web Spam
We provided several examples
Detection
Asked to classify normal / borderline / spam
Topological Web
Spam
Do they agree? Mostly . . .
Direct Counting
of Supporters
Spam Detection
Results
126. Link Analysis on
Agreement between humans
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
127. Link Analysis on
Result: first public Web Spam collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Public spam collection
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
128. Link Analysis on
Result: first public Web Spam collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Public spam collection
Functional
Rankings
Web graph with ∼80 million pages
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
129. Link Analysis on
Result: first public Web Spam collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Public spam collection
Functional
Rankings
Web graph with ∼80 million pages
Web Spam
∼11,000 hosts
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
130. Link Analysis on
Result: first public Web Spam collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Public spam collection
Functional
Rankings
Web graph with ∼80 million pages
Web Spam
∼11,000 hosts
Web Spam
Labels for ∼4,000 hosts by at least 2 humans each
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
131. Link Analysis on
Result: first public Web Spam collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Public spam collection
Functional
Rankings
Web graph with ∼80 million pages
Web Spam
∼11,000 hosts
Web Spam
Labels for ∼4,000 hosts by at least 2 humans each
Detection
Topological Web
Upcoming Web Spam challenge
Spam
Direct Counting
of Supporters
Spam Detection
Results
132. Link Analysis on
Result: first public Web Spam collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Public spam collection
Functional
Rankings
Web graph with ∼80 million pages
Web Spam
∼11,000 hosts
Web Spam
Labels for ∼4,000 hosts by at least 2 humans each
Detection
Topological Web
Upcoming Web Spam challenge
Spam
Machine learning
Direct Counting
of Supporters
Spam Detection
Results
133. Link Analysis on
Result: first public Web Spam collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Public spam collection
Functional
Rankings
Web graph with ∼80 million pages
Web Spam
∼11,000 hosts
Web Spam
Labels for ∼4,000 hosts by at least 2 humans each
Detection
Topological Web
Upcoming Web Spam challenge
Spam
Machine learning
Direct Counting
of Supporters Information retrieval
Spam Detection
Results
134. Link Analysis on
Result: first public Web Spam collection
the Web
Levels of Link
Analysis
Generalizing
PageRank
Other
Public spam collection
Functional
Rankings
Web graph with ∼80 million pages
Web Spam
∼11,000 hosts
Web Spam
Labels for ∼4,000 hosts by at least 2 humans each
Detection
Topological Web
Upcoming Web Spam challenge
Spam
Machine learning
Direct Counting
of Supporters Information retrieval
Spam Detection
webspam-announces-subscribe@yahoogroups.com
Results
135. Link Analysis on
the Web
Levels of Link
Thank you!
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
136. Link Analysis on
the Web
Levels of Link
Thank you!
Analysis
Generalizing
PageRank
Other
Functional
Rankings
Web Spam
Web Spam
Detection
Topological Web
Spam
Direct Counting
of Supporters
Spam Detection
Results
137. Link Analysis on
the Web
Baeza-Yates, R., Boldi, P., and Castillo, C. (2006a).
Generalizing pagerank: Damping functions for link-based
Levels of Link
Analysis
ranking algorithms.
Generalizing
In Proceedings of ACM SIGIR, pages 308–315, Seattle,
PageRank
Washington, USA. ACM Press.
Other
Functional
Rankings
Baeza-Yates, R., Castillo, C., and Efthimiadis, E. (2006b).
Web Spam
Characterization of national web domains.
Web Spam
Detection
To appear in ACM TOIT.
Topological Web
Spam
Baeza-Yates, R. and Poblete, B. (2006).
Direct Counting
of Supporters
Dynamics of the chilean web structure.
Spam Detection
Comput. Networks, 50(10):1464–1473.
Results
Barab´si, A.-L. (2002).
a
Linked: The New Science of Networks.
Perseus Books Group.
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the Web
Becchetti, L., Castillo, C., Donato, D., Leonardi, S., and
Baeza-Yates, R. (2006a).
Levels of Link
Link-based characterization and detection of Web Spam.
Analysis
Generalizing
In Second International Workshop on Adversarial Information
PageRank
Retrieval on the Web (AIRWeb), Seattle, USA.
Other
Functional
Rankings
Becchetti, L., Castillo, C., Donato, D., Leonardi, S., and
Web Spam
Baeza-Yates, R. (2006b).
Web Spam
Using rank propagation and probabilistic counting for
Detection
link-based spam detection.
Topological Web
Spam
In Proceedings of the Workshop on Web Mining and Web
Direct Counting
Usage Analysis (WebKDD), Pennsylvania, USA. ACM Press.
of Supporters
Spam Detection
Bencz´r, A. A., Csalog´ny, K., Sarl´s, T., and Uher, M.
u a o
Results
(2005).
Spamrank: fully automatic link spam detection.
In Proceedings of the First International Workshop on
Adversarial Information Retrieval on the Web, Chiba, Japan.
139. Link Analysis on
the Web
Boldi, P., Santini, M., and Vigna, S. (2005).
Pagerank as a function of the damping factor.
Levels of Link
Analysis
In Proceedings of the 14th international conference on World
Generalizing
Wide Web, pages 557–566, Chiba, Japan. ACM Press.
PageRank
Other
Functional
Broder, A., Kumar, R., Maghoul, F., Raghavan, P.,
Rankings
Rajagopalan, S., Stata, R., Tomkins, A., and Wiener, J.
Web Spam
(2000).
Web Spam
Detection
Graph structure in the web: Experiments and models.
Topological Web
In Proceedings of the Ninth Conference on World Wide Web,
Spam
pages 309–320, Amsterdam, Netherlands. ACM Press.
Direct Counting
of Supporters
Fetterly, D., Manasse, M., and Najork, M. (2004).
Spam Detection
Results
Spam, damn spam, and statistics: Using statistical analysis to
locate spam web pages.
In Proceedings of the seventh workshop on the Web and
databases (WebDB), pages 1–6, Paris, France.
140. Link Analysis on
Flajolet, P. and Martin, N. G. (1985).
the Web
Probabilistic counting algorithms for data base applications.
Levels of Link
Journal of Computer and System Sciences, 31(2):182–209.
Analysis
Generalizing
Gibson, D., Kumar, R., and Tomkins, A. (2005).
PageRank
Other
Discovering large dense subgraphs in massive graphs.
Functional
Rankings
In VLDB ’05: Proceedings of the 31st international conference
Web Spam
on Very large data bases, pages 721–732. VLDB Endowment.
Web Spam
Detection
Gy¨ngyi, Z., Molina, H. G., and Pedersen, J. (2004).
o
Topological Web
Combating web spam with trustrank.
Spam
Direct Counting
In Proceedings of the Thirtieth International Conference on
of Supporters
Very Large Data Bases (VLDB), pages 576–587, Toronto,
Spam Detection
Canada. Morgan Kaufmann.
Results
Newman, M. E., Strogatz, S. H., and Watts, D. J. (2001).
Random graphs with arbitrary degree distributions and their
applications.
Phys Rev E Stat Nonlin Soft Matter Phys, 64(2 Pt 2).
141. Link Analysis on
the Web
Levels of Link
Analysis
Palmer, C. R., Gibbons, P. B., and Faloutsos, C. (2002).
Generalizing
PageRank
ANF: a fast and scalable tool for data mining in massive
Other
Functional
graphs.
Rankings
In Proceedings of the eighth ACM SIGKDD international
Web Spam
conference on Knowledge discovery and data mining, pages
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Detection
81–90, New York, NY, USA. ACM Press.
Topological Web
Spam
Tauro, L., Palmer, C., Siganos, G., and Faloutsos, M. (2001).
Direct Counting
A simple conceptual model for the internet topology.
of Supporters
Spam Detection
In Global Internet, San Antonio, Texas, USA. IEEE CS Press.
Results