Graph Mining

Laws, Tools, and Case Studies

Nonfiction, Computers, Database Management, Data Processing
Cover of the book Graph Mining by Deepayan Chakrabarti, Christos Faloutsos, Morgan & Claypool Publishers
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Deepayan Chakrabarti, Christos Faloutsos ISBN: 9781608451166
Publisher: Morgan & Claypool Publishers Publication: October 1, 2012
Imprint: Morgan & Claypool Publishers Language: English
Author: Deepayan Chakrabarti, Christos Faloutsos
ISBN: 9781608451166
Publisher: Morgan & Claypool Publishers
Publication: October 1, 2012
Imprint: Morgan & Claypool Publishers
Language: English

What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions

More books from Morgan & Claypool Publishers

Cover of the book Provenance Data in Social Media by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Domain-Sensitive Temporal Tagging by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Sentiment Analysis and Opinion Mining by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Hyperbolic Metamaterials by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Arduino Microcontroller Processing for Everyone! by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Women and Physics by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Multitasking in the Digital Age by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Model-Driven Software Engineering in Practice by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Researching Serendipity in Digital Information Environments by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Concepts in Physical Metallurgy by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book The Electric Dipole Moment Challenge by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Essential Semiconductor Laser Physics by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Linked Data by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book Robot Learning from Human Teachers by Deepayan Chakrabarti, Christos Faloutsos
Cover of the book P2P Techniques for Decentralized Applications by Deepayan Chakrabarti, Christos Faloutsos
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy