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.
This presentation won me the best presentation award at my University Tech fest "Allegretto" in 2008.
I have also presented this seminar as a part of B.Tech curriculum in 7th Semester.
This presentation won me the best presentation award at my University Tech fest "Allegretto" in 2008.
I have also presented this seminar as a part of B.Tech curriculum in 7th Semester.
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.
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.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
The PageRank and HITS techniques are used for ranking the relevancy of web pages, through analysis of the hyperlink structure that links pages together
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
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.
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.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
The PageRank and HITS techniques are used for ranking the relevancy of web pages, through analysis of the hyperlink structure that links pages together
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
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.
The PageRank algorithm is an important algorithm which is implemented to determine the quality of a page on the web. With search engines attaining a high position in guiding the traffic on the internet, PageRank is an important factor to determine its flow. Since link analysis is used in search engine's ranking systems, link based spam structure known as link farms are created by spammers to generate a high PageRank for their and in turn a target page. In this paper, we suggest a method through which these structures can be detected and thus the overall ranking results can be improved.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
2. Introduction General outline
Search Optimization History
Pagerank
Conclusion
History
What is Search Optimization
PageRank (Two different notations)
Effects of the Links
How is PageRank Calculated
Advantages and Limitations
3. Introduction General outline
Search Optimization History
Pagerank
Conclusion
Seeds of Search Optimization
Pagerank
Citations analysis, HITS, Hyper Search
Vannevar Bush Gerard Salton Sergei Brin Larry Page
4. Introduction
Search Optimization What is Search optimization
Pagerank Optimization in various Search Engines
Conclusion
The old Approach
Meaning of Search Optimization
It’s a backend Process
Relevancy of the keywords
Link Analysis
Age of a webpage
5. Introduction
Search Optimization What is Search optimization
Pagerank Optimization in various Search Engines
Conclusion
Reciprocal links
Commercial links Natural links
Natural citations Old sites
Content filters
Link Quality-crawl depth
Natural/artificial links
Relevancy algorithms poor Topical community
Biased towards commercial result
Reciprocal links
Page content
6. Introduction Understanding PageRank ( How it works 1 )
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
A social example
Teacher A
Principal Student A
Student B
Teacher B
Student C
7. Introduction Understanding PageRank ( How it works 2 )
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
8. Introduction Understanding PageRank ( Algorithm )
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
PR (A) = (1-d) + d (PR (T1) / C (T1) + ... + PR (Tn) / C (Tn))
Where,
PR(A) is the PageRank of page A
PR(Ti) is the PageRank of pages Ti which link to page A
C(Ti) is the number of outbound links on page Ti and
d is a damping factor which can be set between 0 and 1
In simple terms,
PageRank for a given page = Initial PageRank + (total ranking power ÷
number of outbound links) +...
The second version,
PR (A) = (1-d) + d (PR (T1) / C (T1) + ... + PR (Tn) / C
(Tn))
N
9. Introduction Understanding PageRank ( Damping factor )
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
The random surfer model
Damping Factor d
Minimum PageRank ( 1-d )
Link 1
Maximum PageRank N+( 1-d )
Link 2
Link 1 Link 3
. .
. .
Link n d
Link n
10. Introduction Understanding PageRank ( Computation of PageRank )
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
Consider an imaginary web of 3 web
pages.
And the inbound and outbound link
structure is as shown in the figure. The
calculations can be done by following
method :
PR(A) = 0.5 + 0.5 PR(C) PR(B) = 0.5 + 0.5 (PR(A) / 2) PR(C) = 0.5 + 0.5 ((PR(A) / 2 )+ PR (B))
= 0.5 + (0.5*1)
=1 = 0.5 + 0.5 (1/2) = 0.5 + 0.5 (1/2 + 0.75)
= 0.5 + (0.5 * 0.5) = 0.5 + 0.5 (1.25)
= 0.5 + 0.25 = 0.5 + 0.625
= 0.75 = 1.125
12. Introduction Understanding PageRank ( Effect of inbound links 1 )
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
External Site A
0.22
About
0.41
Home
External Site B
0.92 0.22
Home Product
0.92 0.41
External Site C
0.22
Links
0.41
Home
External Site A
0.92 0.22
Average PR = 0.378
13. Introduction Understanding PageRank ( Effect of inbound links 2 )
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
External Site A
0.34
About
1.1
Home
External Site B
0.92 0.34
Home Product
3.35 1.1
External Site C
0.34
Links
1.1
Home
External Site A
0.92 0.34
Average PR = 3.35
14. Introduction Understanding PageRank ( Effect of outbound links )
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
External Site A
0.23
About
0.84
External Site B
0.23
Home Product
2.44 0.84
External Site C
0.23
Links
0.84
External Site A
0.23
Review A Review B Review C Review D
0.23 0.23 0.23 0.23
15. Introduction Understanding PageRank
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
SERP Rank
Google Toolbar(The intentional surfer model)
Ecosystem
Academic doctoral programs
ISI impact factor(Institute for scientific information)
Wikipedia
16. Introduction Understanding PageRank
Search Optimization Applications
Pagerank Advantages and Limitations
Conclusion
Advantages Limitations
Most relevant search results Bias towards older pages
Reduces spamdexing Link trade
Values Natural Links
17. Introduction
Search Optimization
Pagerank
Conclusion
Optimization is necessary
PageRank most efficient
Web masters' Point of view on PageRank
Google’s Point of view on PageRank
Effect on the web development industry
