SlideShare a Scribd company logo
The Page Rank Axioms Based on  Ranking Systems: The PageRank Axioms ,   by Alon Altman and Moshe Tennenholtz. Presented by Aron Matskin
[object Object],[object Object],[object Object],[object Object]
Talking Points ,[object Object],[object Object],[object Object],[object Object]
Ranking: What ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ranking: How ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ranking Systems’ Properties ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agents Ranking Themselves ,[object Object],[object Object],[object Object],[object Object],[object Object]
Ranking: Problems and Issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ranking Systems: Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object]
Social Choice Theory ,[object Object],[object Object],[object Object]
PageRank Method ,[object Object],[object Object]
PageRank: Intuition ,[object Object],[object Object],[object Object],[object Object],b=2 c=1 a=2 1 1 1 1
PageRank as Random Walk ,[object Object],[object Object]
PageRank: Some Math ,[object Object],b c a a b c a b c G A G ½ 0 0 ½ 0 1 0 1 0
PageRank: Some Math ,[object Object],A G   r = r ,[object Object],[object Object],The solution r is the rank vector.
Calculating PageRank ,[object Object],[object Object],[object Object],[object Object]
PageRank: The Good News ,[object Object],[object Object],[object Object],[object Object],[object Object]
PageRank: The Bad News ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Representation Theorem ,[object Object],[object Object],[object Object],[object Object]
Ranking Systems Defined ,[object Object]
Ranking Systems: Example ,[object Object],G = MyRank(G): c = a < b PageRank(G): c < a = b b c a
Axiom 1: Isomorphism (ISO) ,[object Object],[object Object],b e a g f j i h e = f = g = h = i = j a = b
Axiom 2: Self Edge (SE) ,[object Object],[object Object],[object Object]
Axiom 3: Vote by Committee (VBC) a c b a c b ,[object Object],[object Object]
Axiom 4: Collapsing (COL) b a b ,[object Object],[object Object],[object Object]
Axiom 5: Proxy (PRO) ,[object Object],[object Object],[object Object],x = =
Useful Properties: DEL ,[object Object],[object Object],[object Object],a c b d a c d
DEL: Proof a c b d c b d a VBC
DEL: Proof c b d a VBC c b d a
DEL: Proof ISO,PRO c b d a c b d a
DEL: Proof PRO c d a c b d a
DEL: Proof PRO c d a c d a
DEL: Proof VBC c d a c d a
DEL: Proof VBC c d a a c d
DEL for Self-Edge ,[object Object],a a
Useful Properties: DELETE ,[object Object],[object Object],x = = = =
DELETE: Proof x = = = = COL x y
DELETE: Proof PRO x y
Useful Properties: DUPLICATE ,[object Object],[object Object],c b d a c b d a
DUPLICATE: Proof c b d a c b d a VBC
DUPLICATE: Proof c b d a VBC c b d a
DUPLICATE: Proof c b d a COL c b d a
DUPLICATE: Proof c b d a ISO,PRO c b d a
DUPLICATE: Proof c b d a COL -1 c b d a
DUPLICATE: Proof VBC -1 c b d a c b d a
The Representation Theorem   Proof ,[object Object],[object Object],[object Object]
Proof by Example on  b  and  d b c a a b c a b c G A G d d d R G a b c d 0 1 1 0 0 0 0 ⅓ ½ 0 0 ⅓ ½ 0 0 ⅓ 4 1 3 3
Step 1: Insert Nodes ,[object Object],b c a d b c a d
Step 2: Choose Node to Remove b c a d
Step 3: Remove “self-edges” b c a d
Step 4: Duplicate Predecessors b c a d
Step 5: DELETE the Node b c d
Step 5: DELETE the Extras ,[object Object],b c d
Step 2: Choose Node to Remove ,[object Object],b c d
Step 5: DELETE the Node b d
Step 6: DELETE the Extras ,[object Object],b d
Step 7: Balance by Duplication ,[object Object],b d
Step 8: Equalize by Reverse DEL b d By ISO b=d. By DEL and SE: in G’ b<d.
Example for  a  and  d b c a d b c a d
After Removal of  c b a d
Duplicate Predecessors of  b b a d
DELETE  b a d
DELETE Extras a d
Before Balancing a d
After Balancing a d Conclusion: a<d.
What about  a  and  b ? b a d
What about  a  and  b ? b a d
What about  a  and  b ? b a
What about  a  and  b ? b a
What about  a  and  b ? b a
What about  a  and  b ? b a Conclusion: a=b.
Concluding Remarks ,[object Object]
The End c b d a ½ 0 0 ½ 0 1 0 1 0 a b c a b c

More Related Content

Similar to Ranking systems

[ICDE 2012] On Top-k Structural Similarity Search
[ICDE 2012] On Top-k Structural Similarity Search[ICDE 2012] On Top-k Structural Similarity Search
[ICDE 2012] On Top-k Structural Similarity SearchPei Lee
 
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16MLconf
 
Markov chains and page rankGraphs.pdf
Markov chains and page rankGraphs.pdfMarkov chains and page rankGraphs.pdf
Markov chains and page rankGraphs.pdfrayyverma
 
Analysis Of Algorithm
Analysis Of AlgorithmAnalysis Of Algorithm
Analysis Of AlgorithmBashi9675
 
Lec5 Pagerank
Lec5 PagerankLec5 Pagerank
Lec5 Pagerankmobius.cn
 
Lec5 pagerank
Lec5 pagerankLec5 pagerank
Lec5 pagerankCarlos
 
Pagerank (from Google)
Pagerank (from Google)Pagerank (from Google)
Pagerank (from Google)Sri Prasanna
 
GraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQLGraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQLSpark Summit
 
1 chayes
1 chayes1 chayes
1 chayesYandex
 
Optimized interleaving for online retrieval evaluation
Optimized interleaving for online retrieval evaluationOptimized interleaving for online retrieval evaluation
Optimized interleaving for online retrieval evaluationHan Jiang
 
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames: Graph Queries in Spark SQL by Ankur DaveGraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames: Graph Queries in Spark SQL by Ankur DaveSpark Summit
 
Web Crawling and Reinforcement Learning
Web Crawling and Reinforcement LearningWeb Crawling and Reinforcement Learning
Web Crawling and Reinforcement LearningFrancesco Gadaleta
 
PageRank Algorithm In data mining
PageRank Algorithm In data miningPageRank Algorithm In data mining
PageRank Algorithm In data miningMai Mustafa
 
Degree Sequence Bounds - ICDT 2023 - Final.pptx
Degree Sequence Bounds - ICDT 2023 - Final.pptxDegree Sequence Bounds - ICDT 2023 - Final.pptx
Degree Sequence Bounds - ICDT 2023 - Final.pptxKyleDeeds2
 
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...Mahdi Cherif
 
Lightweight Distributed Trust Propagation
Lightweight Distributed Trust PropagationLightweight Distributed Trust Propagation
Lightweight Distributed Trust PropagationDaniele Quercia
 

Similar to Ranking systems (20)

[ICDE 2012] On Top-k Structural Similarity Search
[ICDE 2012] On Top-k Structural Similarity Search[ICDE 2012] On Top-k Structural Similarity Search
[ICDE 2012] On Top-k Structural Similarity Search
 
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
 
Markov chains and page rankGraphs.pdf
Markov chains and page rankGraphs.pdfMarkov chains and page rankGraphs.pdf
Markov chains and page rankGraphs.pdf
 
Analysis Of Algorithm
Analysis Of AlgorithmAnalysis Of Algorithm
Analysis Of Algorithm
 
Lec5 Pagerank
Lec5 PagerankLec5 Pagerank
Lec5 Pagerank
 
Lec5 Pagerank
Lec5 PagerankLec5 Pagerank
Lec5 Pagerank
 
Lec5 pagerank
Lec5 pagerankLec5 pagerank
Lec5 pagerank
 
Pagerank (from Google)
Pagerank (from Google)Pagerank (from Google)
Pagerank (from Google)
 
GraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQLGraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQL
 
1 chayes
1 chayes1 chayes
1 chayes
 
Optimized interleaving for online retrieval evaluation
Optimized interleaving for online retrieval evaluationOptimized interleaving for online retrieval evaluation
Optimized interleaving for online retrieval evaluation
 
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames: Graph Queries in Spark SQL by Ankur DaveGraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
 
Web Crawling and Reinforcement Learning
Web Crawling and Reinforcement LearningWeb Crawling and Reinforcement Learning
Web Crawling and Reinforcement Learning
 
PageRank Algorithm In data mining
PageRank Algorithm In data miningPageRank Algorithm In data mining
PageRank Algorithm In data mining
 
random test
random testrandom test
random test
 
PHP
PHPPHP
PHP
 
Link Analysis
Link AnalysisLink Analysis
Link Analysis
 
Degree Sequence Bounds - ICDT 2023 - Final.pptx
Degree Sequence Bounds - ICDT 2023 - Final.pptxDegree Sequence Bounds - ICDT 2023 - Final.pptx
Degree Sequence Bounds - ICDT 2023 - Final.pptx
 
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
 
Lightweight Distributed Trust Propagation
Lightweight Distributed Trust PropagationLightweight Distributed Trust Propagation
Lightweight Distributed Trust Propagation
 

Recently uploaded

Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...FIDO Alliance
 
Introduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG EvaluationIntroduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG EvaluationZilliz
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?Mark Billinghurst
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfFIDO Alliance
 
Strategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsStrategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsUXDXConf
 
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptxIOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptxAbida Shariff
 
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Julian Hyde
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101vincent683379
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyJohn Staveley
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyUXDXConf
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomCzechDreamin
 
UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2DianaGray10
 
Buy Epson EcoTank L3210 Colour Printer Online.pdf
Buy Epson EcoTank L3210 Colour Printer Online.pdfBuy Epson EcoTank L3210 Colour Printer Online.pdf
Buy Epson EcoTank L3210 Colour Printer Online.pdfEasyPrinterHelp
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessUXDXConf
 
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlFuture Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlPeter Udo Diehl
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityScyllaDB
 
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfHow Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfFIDO Alliance
 
Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024TopCSSGallery
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfFIDO Alliance
 
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptxUnpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptxDavid Michel
 

Recently uploaded (20)

Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
 
Introduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG EvaluationIntroduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG Evaluation
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
Strategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsStrategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering Teams
 
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptxIOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
 
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John Staveley
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
 
UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2
 
Buy Epson EcoTank L3210 Colour Printer Online.pdf
Buy Epson EcoTank L3210 Colour Printer Online.pdfBuy Epson EcoTank L3210 Colour Printer Online.pdf
Buy Epson EcoTank L3210 Colour Printer Online.pdf
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
 
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlFuture Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through Observability
 
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfHow Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
 
Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
 
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptxUnpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
 

Ranking systems

Editor's Notes

  1. Booby Fischer was #49 on PCA ratings list in 1994, although he had not played for 20 years