Community detection and mining in social media
By: Lei Tang, Huan Liu
Contributors: Liu, Huan
Series: Synthesis lectures on data mining and knowledge discovery -- # 3
Publisher: Morgan & Claypool
Published: 2010
Place of publication: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA)
Language: eng
Format: [electronic resource] /
ISBN-13: 9781608453559; 9781608453542
Subjects: Social media; Web usage mining
Other titles: Synthesis digital library of engineering and computer science.
Description: The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale.This lecture, 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 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.
Notes: Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 105-115) and index.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.
Table of contents: title: Acknowledgments; title: 1. Social media and social computing; title: Social media; title: Concepts and definitions; title: Networks and representations; title: Properties of large-scale networks; title: Challenges; title: Social computing tasks; title: Network modeling; title: Centrality analysis and influence modeling; title: Community detection; title: Classification and recommendation; title: Privacy, spam and security; title: Summary; title: 2. Nodes, ties, and influence; title: Importance of nodes; title: Strengths of ties; title: Learning from network topology; title: Learning from user attributes and interactions; title: Learning from sequence of user activities; title: Influence modeling; title: Linear threshold model (LTM); title: Independent cascade model (ICM); title: Influence maximization; title: Distinguishing influence and correlation; title: 3. Community detection and evaluation; title: Node-centric community detection; title: Complete mutuality; title: Reachability; title: Group-centric community detection; title: Network-centric community detection; title: Vertex similarity; title: Latent space models; title: Block model approximation; title: Spectral clustering; title: Modularity maximization; title: A unified process; title: Hierarchy-centric community detection; title: Divisive hierarchical clustering; title: Agglomerative hierarchical clustering; title: Community evaluation; title: 4. Communities in heterogeneous networks; title: Heterogeneous networks; title: Multi-dimensional networks; title: Network integration; title: Utility integration; title: Feature integration; title: Partition integration; title: Multi-mode networks; title: Co-clustering on two-mode networks; title: Generalization to multi-mode networks; title: 5. Social media mining; title: Evolution patterns in social media; title: A naive approach to studying community evolution; title: Community evolution in smoothly evolving networks; title: Segment-based clustering with evolving networks; title: Classification with network data; title: Collective classification; title: Community-based learning; title: Summary; title: A. Data collection; title: B. Computing betweenness; title: C. K-means clustering; title: Bibliography; title: Authors' biographies; title: Index.