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Graph Mining Laws Tools And Case Studies Deepayan Chakrabarti

SKU: BELL-11155012

$ 31.00 ~~$ 45.00~~ (-31%)

4.8

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Graph Mining Laws Tools And Case Studies Deepayan Chakrabarti instant download after payment.

Publisher: Morgan & Claypool Publishers

File Extension: PDF

File size: 4.76 MB

Pages: 208

Author: Deepayan Chakrabarti, Christos Faloutsos

ISBN: 9781608451159, 1608451151

Language: English

Year: 2012

Product desciption

Graph Mining Laws Tools And Case Studies Deepayan Chakrabarti by Deepayan Chakrabarti, Christos Faloutsos 9781608451159, 1608451151 instant download after payment.

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