Community Detection and Mining in Social Media

✍ Scribed by Lei Tang, Huan Liu

• Publisher: Morgan & Claypool
• Year: 2010
• Tongue: English
• Leaves: 137
• Series: Synthesis Lectures on Data Mining and Knowledge Discovery
• Category: Library

✦ Synopsis

This book, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website http://dmml.asu.edu/cdm/ for the latest information. Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining

✦ Table of Contents

Acknowledgments......Page 13
Social Media......Page 15
Networks and Representations......Page 17
Properties of Large-Scale Networks......Page 19
Challenges......Page 20
Network Modeling......Page 21
Community Detection......Page 22
Privacy, Spam and Security......Page 24
Summary......Page 25
Importance of Nodes......Page 27
Learning from Network Topology......Page 32
Learning from User Attributes and Interactions......Page 34
Influence Modeling......Page 35
Linear Threshold Model (LTM)......Page 36
Independent Cascade Model (ICM)......Page 37
Influence Maximization......Page 38
Distinguishing Influence and Correlation......Page 40
Complete Mutuality......Page 45
Reachability......Page 47
Group-Centric Community Detection......Page 48
Vertex Similarity......Page 49
Latent Space Models......Page 51
Block Model Approximation......Page 53
Spectral Clustering......Page 55
Modularity Maximization......Page 57
A Unified Process......Page 59
Divisive Hierarchical Clustering......Page 60
Agglomerative Hierarchical Clustering......Page 62
Community Evaluation......Page 63
Heterogeneous Networks......Page 69
Multi-Dimensional Networks......Page 71
Network Integration......Page 72
Utility Integration......Page 74
Feature Integration......Page 76
Partition Integration......Page 79
Co-Clustering on Two-Mode Networks......Page 82
Generalization to Multi-Mode Networks......Page 85
Evolution Patterns in Social Media......Page 89
A Naive Approach to Studying Community Evolution......Page 90
Community Evolution in Smoothly Evolving Networks......Page 93
Segment-based Clustering with Evolving Networks......Page 96
Classification with Network Data......Page 98
Collective Classification......Page 99
Community-based Learning......Page 101
Summary......Page 106
Data Collection......Page 107
Computing Betweenness......Page 111
k-Means Clustering......Page 115
Bibliography......Page 119
Authors' Biographies......Page 131
Index......Page 133