Graph Mining : Laws, Tools, and Case Studies / by Deepayan Chakrabarti, Christos Faloutsos.

Format:

Book

Author/Creator:

Chakrabarti, Deepayan., Author.

Faloutsos, Christos., Author.

Series:

Synthesis Lectures on Data Mining and Knowledge Discovery, 2151-0075

Language:

English

Subjects (All):

Data mining.

Statistics.

Data Mining and Knowledge Discovery.

Local Subjects:

Data Mining and Knowledge Discovery.

Statistics.

Physical Description:

1 online resource (XVI, 191 p.)

Edition:

1st ed. 2012.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2012.

Summary:

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.

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.

ISBN:

9783031019036

3031019032