PageRank is an algorithm used by the Google web search engine to rank websites in the search engine results. PageRank was named after Larry Page, one of the founders of Google. PageRank is a way of measuring the importance of website pages.
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 document compares different ranking algorithms used by search engines. It summarizes PageRank, HITS, SALSA, Weighted PageRank, Distance Rank, and Topic-Sensitive PageRank algorithms. The document analyzes the objectives, inputs, importance, limitations, and applications of each algorithm. It also provides examples and compares the algorithms based on criteria like year of existence, objective, input parameters, importance, limitations, search engines that use them, and quality of results. The proposed work discussed is improving PageRank to address the problem of dangling pages.
This document provides guidance on improving search skills and evaluating online information. It discusses evaluating a website's domain, extensions and URLs to determine reliability. Search strategies like using Boolean operators and the Alta Vista host and URL commands are explained to help find targeted information. Evaluating an author's credentials, links, and updating websites is advised. Using sites like archive.org's Wayback Machine and easywhois.com can help verify information when author details are missing. Sample search techniques are demonstrated.
The document discusses the history and development of web search engines. It describes how early search engines in 1994 indexed around 100,000 pages while Google grew to index over 8 billion pages by 2005. It also explains the basic components and ranking algorithms of search engines, including PageRank, which calculates the importance of pages based on both the number and quality of inbound links.
The PageRank algorithm was developed by Larry Page and Sergey Brin in 1996 to rank the importance of web pages. It measures a page's importance based on the number and quality of links to it, viewing the web as a directed graph. The algorithm models a random web surfer and calculates the probability of ending up on each page. It has since been refined by Google but remains an important factor in search engine results. Variations of PageRank can also be applied to other networks like ranking NFL teams based on game outcomes.
PageRank is an algorithm created by Google founders Larry Page and Sergey Brin that assigns a numerical weight to web pages to measure their relative importance. It is based on the concept that not all links are equal: naturally more important websites receive more incoming links from other websites. PageRank helped address issues with early search engines that could be easily gamed by repeating keywords. It remains one of 200 factors Google uses to determine search rankings, though other strategies like Google Panda are now also important. The simplified PageRank algorithm calculates the probability that a random user clicking links would arrive at a given page based on its incoming links and their PageRanks.
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 document compares different ranking algorithms used by search engines. It summarizes PageRank, HITS, SALSA, Weighted PageRank, Distance Rank, and Topic-Sensitive PageRank algorithms. The document analyzes the objectives, inputs, importance, limitations, and applications of each algorithm. It also provides examples and compares the algorithms based on criteria like year of existence, objective, input parameters, importance, limitations, search engines that use them, and quality of results. The proposed work discussed is improving PageRank to address the problem of dangling pages.
This document provides guidance on improving search skills and evaluating online information. It discusses evaluating a website's domain, extensions and URLs to determine reliability. Search strategies like using Boolean operators and the Alta Vista host and URL commands are explained to help find targeted information. Evaluating an author's credentials, links, and updating websites is advised. Using sites like archive.org's Wayback Machine and easywhois.com can help verify information when author details are missing. Sample search techniques are demonstrated.
The document discusses the history and development of web search engines. It describes how early search engines in 1994 indexed around 100,000 pages while Google grew to index over 8 billion pages by 2005. It also explains the basic components and ranking algorithms of search engines, including PageRank, which calculates the importance of pages based on both the number and quality of inbound links.
The PageRank algorithm was developed by Larry Page and Sergey Brin in 1996 to rank the importance of web pages. It measures a page's importance based on the number and quality of links to it, viewing the web as a directed graph. The algorithm models a random web surfer and calculates the probability of ending up on each page. It has since been refined by Google but remains an important factor in search engine results. Variations of PageRank can also be applied to other networks like ranking NFL teams based on game outcomes.
PageRank is an algorithm created by Google founders Larry Page and Sergey Brin that assigns a numerical weight to web pages to measure their relative importance. It is based on the concept that not all links are equal: naturally more important websites receive more incoming links from other websites. PageRank helped address issues with early search engines that could be easily gamed by repeating keywords. It remains one of 200 factors Google uses to determine search rankings, though other strategies like Google Panda are now also important. The simplified PageRank algorithm calculates the probability that a random user clicking links would arrive at a given page based on its incoming links and their PageRanks.
PageRank is an algorithm created by Google founders Larry Page and Sergey Brin that assigns a numerical weight to web pages based on the page's importance as measured by the quantity and quality of links to that page. It works by assessing a random user's likelihood of landing on any given page if they keep randomly clicking on links. While PageRank is no longer Google's only ranking factor, the number of backlinks from important pages still influences a page's position in search results.
The document provides an overview of search engine optimization (SEO) concepts, including:
1) The importance of SEO for driving online and offline sales.
2) How search engines work and are composed of web crawlers and databases to index web pages.
3) Key factors search engines use to evaluate and rank pages, such as relevance, importance, links, and content.
4) Techniques for improving rankings, like optimizing titles, meta tags, and adding relevant and quality backlinks.
PageRank was developed by Larry Page and Sergey Brin in 1998 to rate the importance of web pages. It uses the link structure of the web to determine a ranking such that pages linked to by many important pages receive a higher ranking. The algorithm models the behavior of a random web surfer who gets bored and jumps to random pages. Google's search engine was built using PageRank to order search results by importance.
Hi All,
This Presentation will feature more about the working of search engine how do the inner functionality takes place. In the later half of the Presentation the Page Rank will be explained in depth. how do they calculate it, How it differing from the actual PR, Google PR. How frequently they do update the PR value in the google. and lots more with calculation and few examples.
Development of a system that automatically generates (kind of) storylines out of social media aggregated around hashtags, following links being shared.
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.
PageRank is an algorithm created by Google's founders to rank the importance of websites in the network of links on the internet. It uses a probability-based model to determine the likelihood that a random user would arrive at a given page. PageRank is calculated through an iterative process of evaluating the inbound links from other pages, with more weight given to pages that are already highly ranked. The example demonstrates how PageRank is computed for a simple network of four pages, with the highest ranking going to the page that receives a link from the page with the strongest inbound links.
Final Google PageRank
The description of Google PageRank, credit to
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDQQFjAB&url=http%3A%2F%2Fwww.cis.temple.edu%2F~vasilis%2FCourses%2FCIS664%2FPapers%2FAn-google.ppt&ei=DM5SU9fqCqTLsQTJvoHIDQ&usg=AFQjCNET09HYwKVkLOQLMmwC4mF0_t5zOw&sig2=2baeAxUEb7h6L-aVIDhwMg&bvm=bv.65058239,d.cWc
Computer study lesson - Internet Search (25 Mar 2020)wmsklang
Here are the answers to your homework questions:
1. Magnets work by the alignment of atomic or subatomic particles called domains that are polarized (given a magnetic "charge"). The magnetic fields of these polarized domains interact and attract or repel other magnetic materials.
2. A spark plug is a device for delivering electric current from an ignition system to the combustion chamber of a spark-ignition engine to ignite the compressed fuel-air mixture by an electric spark, thereby initiating combustion.
3. A light year is the distance that light travels in one year. Since light travels at about 300,000 kilometers (186,000 miles) per second, one light year equals about 9.46 trillion kilometers or 5.88 trillion
Actively Learning to Rank Semantic Associations for Personalized Contextual E...Federico Bianchi
The Semantic Web. ESWC 2017. Lecture Notes in Computer Science, vol 10249. Springer, Cham
Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be extracted from a text, a first challenge in these applications is to effectively determine which SAs are most interesting to the users, defining a suitable ranking function over SAs. However, since different users may have different interests, an additional challenge is to personalize this ranking function to match individual users’ preferences. In this paper we introduce a novel active learning to rank model to let a user rate small samples of SAs, which are used to iteratively learn a personalized ranking function. Experiments conducted with two data sets show that the approach is able to improve the quality of the ranking function with a limited number of user interactions.
San Diego Meetup - Sem Web Overview - 2009.04.27Eric Franzon
This document introduces semantic technologies and the semantic web. It explains that the semantic web (Web 3.0) aims to link data on the web through the use of unique identifiers and relationships between things represented as triples. It provides examples of triples and how they can be used to represent relationships between entities. It also gives an overview of RDF, schemas for linked data, and the SPARQL query language for querying linked data.
SEO (search engine optimization) involves optimizing websites and webpages to appear high in search engine results. It includes ensuring websites are indexed by search engine bots and that all content pages are visible. SEO brings together marketing and strategy - while original, optimized content is important, performance means little without good marketing and a solid strategy. Key factors search engines consider include page rank, backlinks, meta tags, and keyword optimization.
This document provides guidance to students on conducting effective online searches. It discusses the differences between search engines and search directories, and introduces Boolean logic operators like AND, OR and NOT to refine searches. Examples are given of how to use these operators to narrow search results. The document also lists 5 keys to evaluating the credibility of websites, such as checking the URL, author credentials, documentation of sources, and what other sites say about the page.
The document discusses a survey of the "deep web", which refers to content hidden behind query forms on databases rather than static web pages. It finds that current search engines cannot access most of the data on the internet as it resides in the deep web behind database query interfaces. The survey estimates that there are 43,000-96,000 deep web sites containing an estimated 7,500 terabytes of data, around 500 times larger than the visible surface web. It aims to better understand and quantify the scale and characteristics of the deep web which remains largely unexplored compared to the surface web.
Interactive Marketing: The Trends with Content MarketingAshley Segura
The document discusses trends in content marketing and strategies used by successful companies. It notes that Pixar does not start projects with great ideas, but with "ugly babies" that are improved through iterations. It also discusses how content needs are changing with the rise of voice search and mobile usage. Companies are encouraged to focus on emotional storytelling over keywords alone and to reuse evergreen content by adapting it to different formats. Metrics like reading time are more useful than shares for evaluating content.
This presentation is based on alan november’s bookcampbelltricia
This document provides definitions and explanations of key internet concepts like links, homepages, domains, and search engines. It discusses how the internet works by allowing browsers to access IP addresses and domain names. It also explains how to evaluate websites using the REAL criteria: reading URLs, examining content, asking about authors, and looking at links. Students are advised to be aware that not all information online is true and that search engine results aren't always quality-ranked.
This document discusses search engines and web crawling. It begins by defining a search engine as a searchable database that collects information from web pages on the internet by indexing them and storing the results. It then discusses the need for search engines and provides examples. The document outlines how search engines work using spiders to crawl websites, index pages, and power search functionality. It defines web crawlers and their role in crawling websites. Key factors that affect web crawling like robots.txt, sitemaps, and manual submission are covered. Related areas like indexing, searching algorithms, and data mining are summarized. The document demonstrates how crawlers can download full websites and provides examples of open source crawlers.
The document discusses search engines and their history and functioning. It explains that search engines use crawler programs to index web pages and gather keywords to help users find relevant information quickly from the vast World Wide Web. The first search engine Archie was released in 1990 and search engines have since evolved, with companies like Google becoming leaders by consistently improving their algorithms to better understand users' search needs.
Google Desktop is desktop search software that indexes files on a computer and allows users to search emails, files, music, photos and more from a sidebar. It features file indexing, a sidebar with gadgets for email, notes, photos, news and weather, and quick searching across the computer from the sidebar or taskbar. Google Desktop runs on Mac OS X, Linux and Windows and continues to index files in the background as they change.
The document pays tribute to Al-Hussein ibn Ali, referring to him as a friend, original ancestor, and martyr whose promise of return is the closest of all. It asks God to bless and give mercy to Al-Hussein.
PageRank is an algorithm created by Google founders Larry Page and Sergey Brin that assigns a numerical weight to web pages based on the page's importance as measured by the quantity and quality of links to that page. It works by assessing a random user's likelihood of landing on any given page if they keep randomly clicking on links. While PageRank is no longer Google's only ranking factor, the number of backlinks from important pages still influences a page's position in search results.
The document provides an overview of search engine optimization (SEO) concepts, including:
1) The importance of SEO for driving online and offline sales.
2) How search engines work and are composed of web crawlers and databases to index web pages.
3) Key factors search engines use to evaluate and rank pages, such as relevance, importance, links, and content.
4) Techniques for improving rankings, like optimizing titles, meta tags, and adding relevant and quality backlinks.
PageRank was developed by Larry Page and Sergey Brin in 1998 to rate the importance of web pages. It uses the link structure of the web to determine a ranking such that pages linked to by many important pages receive a higher ranking. The algorithm models the behavior of a random web surfer who gets bored and jumps to random pages. Google's search engine was built using PageRank to order search results by importance.
Hi All,
This Presentation will feature more about the working of search engine how do the inner functionality takes place. In the later half of the Presentation the Page Rank will be explained in depth. how do they calculate it, How it differing from the actual PR, Google PR. How frequently they do update the PR value in the google. and lots more with calculation and few examples.
Development of a system that automatically generates (kind of) storylines out of social media aggregated around hashtags, following links being shared.
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.
PageRank is an algorithm created by Google's founders to rank the importance of websites in the network of links on the internet. It uses a probability-based model to determine the likelihood that a random user would arrive at a given page. PageRank is calculated through an iterative process of evaluating the inbound links from other pages, with more weight given to pages that are already highly ranked. The example demonstrates how PageRank is computed for a simple network of four pages, with the highest ranking going to the page that receives a link from the page with the strongest inbound links.
Final Google PageRank
The description of Google PageRank, credit to
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDQQFjAB&url=http%3A%2F%2Fwww.cis.temple.edu%2F~vasilis%2FCourses%2FCIS664%2FPapers%2FAn-google.ppt&ei=DM5SU9fqCqTLsQTJvoHIDQ&usg=AFQjCNET09HYwKVkLOQLMmwC4mF0_t5zOw&sig2=2baeAxUEb7h6L-aVIDhwMg&bvm=bv.65058239,d.cWc
Computer study lesson - Internet Search (25 Mar 2020)wmsklang
Here are the answers to your homework questions:
1. Magnets work by the alignment of atomic or subatomic particles called domains that are polarized (given a magnetic "charge"). The magnetic fields of these polarized domains interact and attract or repel other magnetic materials.
2. A spark plug is a device for delivering electric current from an ignition system to the combustion chamber of a spark-ignition engine to ignite the compressed fuel-air mixture by an electric spark, thereby initiating combustion.
3. A light year is the distance that light travels in one year. Since light travels at about 300,000 kilometers (186,000 miles) per second, one light year equals about 9.46 trillion kilometers or 5.88 trillion
Actively Learning to Rank Semantic Associations for Personalized Contextual E...Federico Bianchi
The Semantic Web. ESWC 2017. Lecture Notes in Computer Science, vol 10249. Springer, Cham
Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be extracted from a text, a first challenge in these applications is to effectively determine which SAs are most interesting to the users, defining a suitable ranking function over SAs. However, since different users may have different interests, an additional challenge is to personalize this ranking function to match individual users’ preferences. In this paper we introduce a novel active learning to rank model to let a user rate small samples of SAs, which are used to iteratively learn a personalized ranking function. Experiments conducted with two data sets show that the approach is able to improve the quality of the ranking function with a limited number of user interactions.
San Diego Meetup - Sem Web Overview - 2009.04.27Eric Franzon
This document introduces semantic technologies and the semantic web. It explains that the semantic web (Web 3.0) aims to link data on the web through the use of unique identifiers and relationships between things represented as triples. It provides examples of triples and how they can be used to represent relationships between entities. It also gives an overview of RDF, schemas for linked data, and the SPARQL query language for querying linked data.
SEO (search engine optimization) involves optimizing websites and webpages to appear high in search engine results. It includes ensuring websites are indexed by search engine bots and that all content pages are visible. SEO brings together marketing and strategy - while original, optimized content is important, performance means little without good marketing and a solid strategy. Key factors search engines consider include page rank, backlinks, meta tags, and keyword optimization.
This document provides guidance to students on conducting effective online searches. It discusses the differences between search engines and search directories, and introduces Boolean logic operators like AND, OR and NOT to refine searches. Examples are given of how to use these operators to narrow search results. The document also lists 5 keys to evaluating the credibility of websites, such as checking the URL, author credentials, documentation of sources, and what other sites say about the page.
The document discusses a survey of the "deep web", which refers to content hidden behind query forms on databases rather than static web pages. It finds that current search engines cannot access most of the data on the internet as it resides in the deep web behind database query interfaces. The survey estimates that there are 43,000-96,000 deep web sites containing an estimated 7,500 terabytes of data, around 500 times larger than the visible surface web. It aims to better understand and quantify the scale and characteristics of the deep web which remains largely unexplored compared to the surface web.
Interactive Marketing: The Trends with Content MarketingAshley Segura
The document discusses trends in content marketing and strategies used by successful companies. It notes that Pixar does not start projects with great ideas, but with "ugly babies" that are improved through iterations. It also discusses how content needs are changing with the rise of voice search and mobile usage. Companies are encouraged to focus on emotional storytelling over keywords alone and to reuse evergreen content by adapting it to different formats. Metrics like reading time are more useful than shares for evaluating content.
This presentation is based on alan november’s bookcampbelltricia
This document provides definitions and explanations of key internet concepts like links, homepages, domains, and search engines. It discusses how the internet works by allowing browsers to access IP addresses and domain names. It also explains how to evaluate websites using the REAL criteria: reading URLs, examining content, asking about authors, and looking at links. Students are advised to be aware that not all information online is true and that search engine results aren't always quality-ranked.
This document discusses search engines and web crawling. It begins by defining a search engine as a searchable database that collects information from web pages on the internet by indexing them and storing the results. It then discusses the need for search engines and provides examples. The document outlines how search engines work using spiders to crawl websites, index pages, and power search functionality. It defines web crawlers and their role in crawling websites. Key factors that affect web crawling like robots.txt, sitemaps, and manual submission are covered. Related areas like indexing, searching algorithms, and data mining are summarized. The document demonstrates how crawlers can download full websites and provides examples of open source crawlers.
The document discusses search engines and their history and functioning. It explains that search engines use crawler programs to index web pages and gather keywords to help users find relevant information quickly from the vast World Wide Web. The first search engine Archie was released in 1990 and search engines have since evolved, with companies like Google becoming leaders by consistently improving their algorithms to better understand users' search needs.
Google Desktop is desktop search software that indexes files on a computer and allows users to search emails, files, music, photos and more from a sidebar. It features file indexing, a sidebar with gadgets for email, notes, photos, news and weather, and quick searching across the computer from the sidebar or taskbar. Google Desktop runs on Mac OS X, Linux and Windows and continues to index files in the background as they change.
The document pays tribute to Al-Hussein ibn Ali, referring to him as a friend, original ancestor, and martyr whose promise of return is the closest of all. It asks God to bless and give mercy to Al-Hussein.
Since the mid-1990s, a new research area called webometrics based on bibliometric andinformetrics methods has been created [Norouzi, 2006]. Webometrics is the quantitativeanalysis of the web phenomenon employing methods of informetrics [Bjoneborn, Ingwersen,2004]. Due to the importance of the Web, some studies dealing with webometrics seek todescribe web and offer diverse statistics about some of the features and capabilities of theWeb sites [Noroozi Chakoli, 2012]. In EERQI analysis, the need for new retrieval andclustering techniques, and webometrics method mentioned (EERQI, 2008).On the other hand, the Web has developed into the most important scholarly communicationtool and has made more and more scientific information accessible [Kargar, 2011].The present paper seeks to investigate the situation of Estonia on the basis of the Rich Filesand to compare it with other CEE countries. On the other hand, it attempts to have a reviewof the status of scientific output, number of publications is an indicator of Scientific power (Vinkler, 1986), of Estonia in Scopus – that is one of the databases for scientific assessment-and to compare it with other CEE countries. Lastly, this paper will check whether there existsa correlation between the number of the CEE countries Rich Files and the number of their scientific product. In other words, we shall attempt to discover if the number of the RichFiles could represent the scientific ranking of a given CEE country?The Rich files, consisting of PDF, DOC, DOCX, PPT, have been singled out as the basis of our analysis because the majority of the scientific products are being issued in this these files formats since Webometrics , in its scientific assessment of universities, employs the number of Rich Files as one of its criteria which calls it Openness.
SID has gathered unique scientific information for 10 years. It provides journal assessment, impact factor, and immediacy index. SID plans to develop science and technology evaluation indicators and researcher assessment measures including number of documents, highly cited researchers, and H-index. It will also assess universities and research centers using metrics like number of documents, citations, citation rate, and collaboration measures. SID aims to expand its assessments to cities, states, countries, and conduct macro assessments using bibliometric indicators and collaboration/specialization measures. It will also develop science visualization methods including charts, maps, and networks to analyze collaboration and citations.
In present societies, knowledge is known as the main source of Economic prosperity and Societies that derive their economical power from the production and diffusion of information and knowledge are referred to as knowledge-based societies or economies. This paper aimed to measure Triple Helix for studying the innovation infrastructure in Iran in compare with Netherlands, Russia, and Turkey. This research is based on Webometrics methods and we performed this research in two ways: first, we used the number of hits and co-occurrence of
“university”, “industry” and “government”.
Second, we
confined our search to Rich Files. In first way; the results show that in selected countries, “University”, “Industry” And “Government” are
more integrated in Netherlands following by Russia, Turkey and Iran in recent years. Iran in compare with other countries has no a good situation. In second way; the results show a different situation. Netherlands has higher value in this indicator, following by Turkey, Iran and Russia.
This document provides an overview and instructions for using AGELINE, a database that focuses on literature related to aging and social gerontology. It indexes over 200 sources and covers topics from health sciences to public policy. The database can be searched by keywords or filtered by subject, date, and other options. Users can view full text articles and citations. It allows highlighting of search terms, saving pages and notes to folders, and other features to aid research on aging-related topics.
The digital object identifier (DOI) system provides a persistent unique identifier for digital objects. A DOI name consists of a prefix assigned to a registrant and a suffix chosen by the registrant. The DOI remains permanently linked to the object even if its location changes. Resolving a DOI provides current metadata and links to access the object.
This document analyzes the relationships between university, industry, and government (the "Triple Helix") in the Netherlands, Russia, Turkey, and Iran using webometrics methods. It finds that in the first method using search hits, the Netherlands shows the most integration between the three sectors followed by Russia, Turkey, and Iran. However, the second method looking at file types finds the Netherlands has the highest value followed by Turkey, Iran, and Russia. The document aims to measure innovation infrastructure in Iran compared to the other countries.
This document provides an overview and instructions for using the SCImago Journal & Country Rank portal, which includes scientific indicators and rankings of journals and countries derived from the Scopus database. It describes how to search and filter journal and country rankings according to subject area, country, year, and other criteria. It also explains the various bibliometric indicators included in the journal and country profiles and comparison tools, such as the SJR indicator, H-index, citations per document, and more. Bubble charts can also be used to analyze and compare national scientific output based on various performance metrics.
This resource provides a comprehensive index of over 700,000 entries from thousands of academic journals, magazines, books and other sources on world history. It indexes historical articles in over 40 languages from 1450 to present. Students and researchers use it to guide their studies by organizing materials by place, time period, subject and other categories. It is considered the standard indexing tool for the literature of history and related social sciences.
PageRank is an algorithm developed by Larry Page and Sergey Brin at Stanford University to rank web pages for Google search results. It determines a page's importance by counting the number and quality of links to a page. PageRank is calculated through an iterative process and spreads importance across the web like a "random surfer" clicking links. Key factors that influence PageRank include internal site structure, external links, and minimizing links out of the site from high PageRank pages. PageRank helped Google revolutionize search and become a multi-billion dollar company by providing more relevant results.
This document provides an overview of PageRank, the algorithm used by Google to rank websites in search results. PageRank is a way to measure a page's importance by analyzing the number and quality of links to it, with more incoming links and links from important pages improving its rank. The algorithm calculates PageRank recursively based on the PageRank scores of incoming pages divided by the number of outgoing links. It also includes a damping factor to account for random surfing. The Google Toolbar displays an approximate PageRank value from 0 to 10 to indicate a page's importance relative to other sites.
PageRank is an algorithm created by Larry Page and Sergey Brin that ranks web pages based on the number and quality of links to a page. It interprets a link from page A to page B as a vote for page B. PageRank is calculated through an iterative process where each page is given an initial ranking that is then recalculated based on the rankings of pages that link to it. The damping factor determines how much a page's ranking is passed on through its outbound links. A higher damping factor results in more equal distribution of ranking across all pages on a site.
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.
PageRank is an algorithm used by Google to determine the importance of websites based on their link structure. It assigns a numerical ranking to each site which indicates the probability that a random user would visit that page. The algorithm models a random web surfer who gets bored and randomly jumps to other pages. It considers both the number and quality of links to a page, with pages getting ranking from other highly ranked pages that link to them. The PageRank of all pages forms a probability distribution and can be calculated iteratively through a damping factor that determines how much ranking is passed through links.
Evaluation of Web Search Engines Based on Ranking of Results and FeaturesWaqas Tariq
Search engines help the user to surf the web. Due to the vast number of web pages it is highly impossible for the user to retrieve the appropriate web page he needs. Thus, Web search ranking algorithms play an important role in ranking web pages so that the user could retrieve the page which is most relevant to the user's query. This paper presents a study of the applicability of two user-effort-sensitive evaluation measures on five Web search engines (Google, Ask, Yahoo, AOL and Bing). Twenty queries were collected from the list of most hit queries in the last year from various search engines and based upon that search engines are evaluated.
This document provides an overview of the PageRank algorithm. It begins with background on PageRank and its development by Brin and Page. It then introduces the concepts behind PageRank, including how it uses the link structure of webpages to determine importance. The core PageRank algorithm is explained, modeling the web as a graph and calculating page importance based on both the number and quality of inbound links. Iterative methods like power iteration are described for approximating solutions. Examples are given to illustrate PageRank calculations over multiple iterations. Implementation details, applications, advantages/disadvantages are also discussed at a high level. Pseudocode is included.
The PageRank algorithm calculates the importance of web pages based on the structure of incoming links. It models a random web surfer that randomly clicks on links, and also occasionally jumps to a random page. Pages are given more importance if they are linked to by other important pages. The algorithm represents this as a Markov chain and computes the PageRank scores through an iterative process until convergence. It has the advantages of being resistant to spam and efficiently pre-computing scores independently of user queries.
The way in which the displaying of the web pages is done within a search is not a mystery. It involves applied math and good computer science knowledge for the right implementation. This relation involves vectors, matrixes and other mathematical notations. The PageRank vector needs to be calculated, that implies calculations for a stationary distribution, stochastic matrix. The matrices hold the link structure and the guidance of the web surfer. As links are added every day, and the number of websites goes beyond billions, the modification of the web link’s structure in the web affects the PageRank. In order to make this work, search algorithms need improvements. Problems and misbehaviors may come into place, but this topic pays attention to many researches which do improvements day by day. Even though it is a simple formula, PageRank runs a successful business. PageRank may be considered as the right example where applied math and computer knowledge can be fitted together.
PageRank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of "measuring" its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotations and references. The numerical weight that it assigns to any given element E is referred to as the PageRank of E and denoted by {\displaystyle PR(E).} PR(E). Other factors like Author Rank can contribute to the importance of an entity.
A PageRank results from a mathematical algorithm based on the webgraph, created by all World Wide Web pages as nodes and hyperlinks as edges, taking into consideration authority hubs such as cnn.com or usa.gov. The rank value indicates an importance of a particular page. A hyperlink to a page counts as a vote of support. The PageRank of a page is defined recursively and depends on the number and PageRank metric of all pages that link to it ("incoming links"). A page that is linked to by many pages with high PageRank receives a high rank itself.
Numerous academic papers concerning PageRank have been published since Page and Brin's original paper.[5] In practice, the PageRank concept may be vulnerable to manipulation. Research has been conducted into identifying falsely influenced PageRank rankings. The goal is to find an effective means of ignoring links from documents with falsely influenced PageRank.
Other link-based ranking algorithms for Web pages include the HITS algorithm invented by Jon Kleinberg (used by Teoma and now Ask.com),the IBM CLEVER project, the TrustRank algorithm and the hummingbird algorithm.
PageRank is the algorithm used by Google to rank web pages for search results. It analyzes the link structure of the web by treating inbound links as votes and ranking pages based on the number and quality of votes they receive from other pages. PageRank relies on the democratic nature of the web and its link structure as an indicator of a page's importance. It models the behavior of a random web surfer who gets bored and jumps to random pages. Google calculates PageRank values for billions of web pages to determine their relative importance and relevance to search queries in a matter of hours. Beyond search, PageRank has applications for reputation systems, collaborative filtering, opinion polls, and analyzing other real-world networks.
PageRank is a method for ranking web pages based on the link structure of the web. It was developed by Google to help search engines make sense of the vast heterogeneity of the World Web. PageRank works by treating individual web pages as nodes and links between pages as edges, and recursively propagating importance weights through this link structure. It helps address issues like some pages having more backlinks but from less important places compared to pages with fewer but highly ranked backlinks. Dangling links that point to pages with no outgoing links are initially removed to avoid them forming rank sinks before final PageRank calculations are made.
PageRank is a method for ranking web pages based on the link structure of the web. It was developed by Google to help search engines make sense of the vast heterogeneity of the World Web. PageRank works by treating individual pages as nodes and links between pages as edges, then recursively propagating importance weights through this link structure. It helps address issues like some pages having more backlinks due to their popularity rather than their actual importance. The algorithm involves iteratively computing PageRanks until they converge based on a damping factor and the number of outbound links from pages.
PageRank is a method for ranking web pages based on the link structure of the web. It was developed by Google to help search engines make sense of the vast heterogeneity of the World Web. PageRank works by treating individual pages as nodes and links between pages as edges, then recursively propagating importance weights through this link structure. It helps address issues like some pages having many low-quality backlinks versus others having a few highly important backlinks. PageRank defines the importance of a page as a damping factor times the sum of the importance of pages linking to it.
PageRank is a method for ranking web pages based on the link structure of the web. It was developed by Google to help search engines make sense of the vast heterogeneity of the World Web. PageRank works by treating individual pages as nodes and links between pages as edges, then recursively propagating importance weights through this link structure. It helps address issues like some pages having many low-quality backlinks versus others having a few highly important backlinks. PageRank models the probability of a person randomly clicking on links by treating it as a random walk through the link graph.
PageRank is a method for ranking web pages based on the link structure of the web. It was developed by Google to help search engines make sense of the vast heterogeneity of the World Web. PageRank works by treating individual pages as nodes and links between pages as edges, then recursively propagating importance weights through this link structure. It helps address issues like some pages having more backlinks due to their popularity rather than their actual importance. The algorithm involves iteratively computing PageRanks until they converge based on a damping factor and the number of outbound links from pages.
PageRank is a method for ranking web pages based on the link structure of the web. It was developed by Google to help search engines make sense of the vast heterogeneity of the World Web. PageRank works by treating individual web pages as nodes and links between pages as edges, and recursively propagating importance weights through this link structure. It helps address issues like some pages having more backlinks due to their popularity rather than their actual importance. The algorithm involves iteratively computing the PageRank scores until they converge based on the link structure and a damping factor.
PageRank is a method for ranking web pages based on the link structure of the web. It was developed by Google to help search engines make sense of the vast heterogeneity of the World Web. PageRank works by treating individual web pages as nodes and links between pages as edges, and recursively propagating importance weights through this link structure. It helps address issues like some pages having more backlinks but from less important places compared to pages with fewer but highly ranked backlinks. Dangling links that point to pages with no outgoing links are initially removed to avoid them forming rank sinks before final PageRank calculations are made.
The relation between the number of countries-Rich Files on the web and countries-economic development
Research in what fields? Determining Iran’s research priorities according to their impact on economic development
سابقه و هدف: با توجه به محدوديت منابع مالی و انسانی تعيين اولويتهای پژوهشی از مهمترين مسائل فراروی سياستگذاران علم و فناوری میباشد. اين تحقيق از ديدگاه اقتصادی به تعيين اولويتهای پژوهشی ايران پرداخته است.
مواد و روشها: اين تحقيق از نوع کاربردی بوده که به روش علم سنجی و اقتصاد سنجي صورت گرفته است. دادههای توليد علم از SCImago و دادههای مربوط به سرانه توليد ناخالص داخلی از پايگاه بانک جهانی استخراج شده است و تحليل دادهها با استفاده از نرمافزار Eviwes7 صورت گرفته است.
يافتهها: گروههای مهندسی و علوم انسانی (در سطح 01/0) بر سرانه توليد ناخالص تاثيرگذار هستند. گروههای علوم انسانی (در سطح 01/0)، دامپزشکی و پزشکی (در سطح 05/0) از سرانه توليد ناخالص تاثير پذير هستند.
نتيجه گيری: رشتههای مهندسی زيست پزشکی، عمران و ساختمان، مهندسی سيستم و نظارت، مهندسی صنايع و توليد، مهندسی مکانيک، مکانيک مواد و علم مواد، بر رشد اقتصادی تاثيرگذار هستند، به عبارت ديگر اين رشتهها میتوانند در اولويت های پژوهشی کشور قرار گيرند.
به منظور توصیف رشد علم، شاخص رشد نسبی انتشارات در سال 2000 توسط وینکلر ارائه شد. این شاخص تعداد مقالات منتشرشده در یک سال مشخص (Py) را به مجموع مقالات انتشاریافته در یک دوره زمانی پیش از سال y (t=y-1) مرتبط میکند.
هدف: وجود منابع علمی در وب معیاری به دستمیدهد که میتوان از آن به منظور ارزیابی علمی بهره برد. هدف اصلی مقاله بررسی رابطه تعداد فایل¬های غنی در وب و تعداد مدارک کشورهای خاورمیانه در پایگاه اسکوپوس است.
روش پژوهش: از اواسط دهه 1990 حوزه پژوهشی جدیدی به نام وبسنجی بر پایهی روشهای کتابسنجی و اطلاعسنجی بهوجودآمد. وبسنجی، تحلیل کمی پدیدهوب با استفاده از روشهای اطلاعسنجی است. این مقاله در حوزه وبسنجی و با روش مقایسه تطبیقی صورتگرفتهاست. جامعه آماری تحقیق کلیه کشورهای خاورمیانه میباشد که در محیط وب دارای فایلهای غنی شده علمی میباشد. برای پاسخ به آزمون از ضریب همبستگی اسپیرمن استفاده شد و به منظور تجزیه و تحلیلدادهها از نرمافزار اس پی اس نسخه 19 استفادهشدهاست.
یافتهها: نتایج تحقیق نشانمیدهد که میان تولید علم کشورهای خاورمیانه و تعداد فایلهای غنی آن کشور روی وب همبستگی بالایی وجوددارد، بر مبنای تعداد فایلهای غنی، به ترتیب ترکیه، رژیم صهیونیستی و ایران در جایگاه اول تا سوم قراردارند.
نتیجهگیری: مقاله حاضر رویکردی نو به منظور ارزیابی علمی در حوزه وبسنجی معرفیمیکند که با توجه به مزایای وبسنجی میتواند در کنار سایر روشها در ارزیابی علمی به کاررود، وجود همبستگی میان نتایج این روش با روش مرسوم ارزیابی علمی نشانمیدهد که این روش میتواند به عنوان روش در ارزیابی علمی به کاربردهشود.
POPLINE is a database established in 1973 that contains over 345,000 records related to population, family planning, and reproductive health. It includes journal articles, reports, and other documents, many of which are unavailable elsewhere. POPLINE covers topics like contraceptive methods, maternal and child health, HIV/AIDS, and demography. It is updated annually with 7,000 new records. Keywords are used to index documents and help users search the database more effectively.
EconPapers is the largest online collection of economics working papers and journal articles, drawing on data from over 1100 archives. It provides access to most full texts for free but some require subscription. Run by Sune Karlsson and hosted at Örebro University, EconPapers utilizes the bibliographic data from RePEc, a distributed dataset maintained across many research organizations and publishers.
Instagram has become one of the most popular social media platforms, allowing people to share photos, videos, and stories with their followers. Sometimes, though, you might want to view someone's story without them knowing.
Gen Z and the marketplaces - let's translate their needsLaura Szabó
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
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Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
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Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
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Discover the benefits of outsourcing SEO to Indiadavidjhones387
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Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
3. What is PR?
PageRank is an algorithm used by the Google web search engine to
rank websites in the search engine results. PageRank was named
after Larry Page, one of the founders of Google. PageRank is a way
of measuring the importance of website pages.
4. How dose it work?
PageRank works by counting the number and quality of links to a
page to determine a rough estimate of how important the website
is. The underlying assumption is that more important websites are
likely to receive more links from other websites.
5.
6. History
The idea of formulating a link analysis problem as a eigenvalue
problem was probably first suggested in 1976 by Gabriel Pinski and
Francis Narin, who worked on scientometrics ranking of scientific
journals. PageRank was developed at Stanford University by Larry
Page and Sergey Brin in 1996 as part of a research project about a new
kind of search engine.
The first paper about the project, describing PageRank and the initial
prototype of the Google search engine, was published in 1998.
PageRank has been influenced by citation analysis, early developed by
Eugene Garfield in the 1950s at the University of Pennsylvania, and by
Hyper Search, developed by Massimo Marchiori at the University of
Padua. In the same year PageRank was introduced (1998), Jon Kleinberg
published his important work on HITS. Google's founders cite Garfield,
Marchiori, and Kleinberg in their original paper.
7. A glance
The name "PageRank" plays off of the name of developer Larry Page, as
well as the concept of a web page. The word is a trademark of Google, and
the PageRank process has been patented (U.S. Patent 6,285,999).
However, the patent is assigned to Stanford University and not to Google.
Google has exclusive license rights on the patent from Stanford University.
The university received 1.8 million shares of Google in exchange for use of
the patent; the shares were sold in 2005 for $336 million.
8. It is not the only algorithm used by Google to order search engine
results, but it is the first algorithm that was used by the company,
and it is the most well-known. Google uses an automated web
spider called Googlebot to actually count links and gather other
information on web pages.
9. Description
PageRank is a link analysis algorithm and it assigns a numerical
weighting to each element of a hyperlinked set of documents, such
as the World Wide Web, with the purpose of "measuring" its relative
importance within the set. The algorithm may be applied to any
collection of entities with reciprocal quotations and references. The
numerical weight that it assigns to any given element E is referred to
as the PageRank of E and denoted by:
PR(E)
10. Description2
A PageRank results from a mathematical algorithm based on the
webgraph, created by all World Wide Web pages as nodes and
hyperlinks as edges, The rank value indicates an importance of a
particular page. A hyperlink to a page counts as a vote of support.
The PageRank of a page is defined recursively and depends on the
number and PageRank metric of all pages that link to it ("incoming
links"). A page that is linked to by many pages with high PageRank
receives a high rank itself. If there are no links to a web page, then
there is no support for that page. The value of incoming links is
known as "Google juice", "link juice" or “Pagerank juice”.
11. Algorithm
PageRank is a probability distribution used to represent the likelihood that a
person randomly clicking on links will arrive at any particular page. PageRank
can be calculated for collections of documents of any size. It is assumed in
several research papers that the distribution is evenly divided among all
documents in the collection at the beginning of the computational process.
The PageRank computations require several passes, called "iterations",
through the collection to adjust approximate PageRank values to more closely
reflect the theoretical true value.
12. A probability is expressed as a numeric value between 0 and 1. A 0.5
probability is commonly expressed as a "50% chance" of something
happening. Hence, a PageRank of 0.5 means there is a 50% chance that a
person clicking on a random link will be directed to the document with the
0.5 PageRank.
13. A simple example
Assume a small universe of four web pages: A, B, C and D. Links from a page
to itself, or multiple outbound links from one single page to another single
page, are ignored. PageRank is initialized to the same value for all pages. In
the original form of PageRank, the sum of PageRank over all pages was the
total number of pages on the web at that time, so each page in this example
would have an initial PageRank of 1. However, later versions of PageRank, and
the remainder of this section, assume a probability distribution between 0 and
1. Hence the initial value for each page is 0.25
14. The PageRank transferred from a given page to the targets of its outbound
links upon the next iteration is divided equally among all outbound links.
15. If the only links in the system were from pages B, C, and D to A, each link
would transfer 0.25 PageRank to A upon the next iteration, for a total of
0.75.
A
C
D
B0.25
0.5
0.75
PR(A) = PR(B) + PR (C) + PR(D)
16. A bit complex
Suppose instead that page B had a link to pages C and A, page C had a link
to page A, and page D had links to all three pages.
18. Thus, upon the next iteration, page B would transfer half of its existing
value, or 0.125, to page A and the other half, or 0.125, to page C. Page C
would transfer all of its existing value, 0.25, to the only page it links to, A.
Since D had three outbound links, it would transfer one third of its existing
value, or approximately 0.083, to A. At the completion of this iteration,
page A will have a PageRank of 0.458.
19. In other words, the PageRank conferred by an outbound link is equal to
the document's own PageRank score divided by the number of outbound
links L( ).
PR(A) = PR(B)/L(B) + PR(C)/L(C) + PR(D)/L(D)
20. In the general case, the PageRank value for any page u can be
expressed as:
PR(u) =
i.e. the PageRank value for a page u is dependent on the PageRank values
for each page v contained in the set Bu (the set containing all pages linking
to page u), divided by the number L(v) of links from page v.
21. Damping factor
The PageRank theory holds that an imaginary surfer who is randomly
clicking on links will eventually stop clicking. The probability, at any step,
that the person will continue is a damping factor d. Various studies have
tested different damping factors, but it is generally assumed that the
damping factor will be set around 0.85.
22. The damping factor is subtracted from 1 (and in some variations of the
algorithm, the result is divided by the number of documents (N) in the
collection) and this term is then added to the product of the damping
factor and the sum of the incoming PageRank scores. That is,
23. Google recalculates PageRank scores each time it crawls the Web and
rebuilds its index. As Google increases the number of documents in its
collection, the initial approximation of PageRank decreases for all
documents.
24. The formula uses a model of a random surfer who gets bored after several
clicks and switches to a random page. The PageRank value of a page
reflects the chance that the random surfer will land on that page by
clicking on a link. It can be understood as a Markov chain in which the
states are pages, and the transitions, which are all equally probable, are
the links between pages.
25. If a page has no links to other pages, it becomes a sink and therefore terminates the
random surfing process. If the random surfer arrives at a sink page, it picks another
URL at random and continues surfing again.
When calculating PageRank, pages with no outbound links are assumed to link out to all
other pages in the collection. Their PageRank scores are therefore divided evenly
among all other pages. In other words, to be fair with pages that are not sinks, these
random transitions are added to all nodes in the Web, with a residual probability usually
set to d = 0.85, estimated from the frequency that an average surfer uses his or her
browser's bookmark feature.
26. So:
where P1,P2,P3,… PN are the pages under consideration, M(Pi) is the set of pages that
link to Pi, L(Pi)is the number of outbound links on page Pi , and N is the total number
of pages.