Community detection and mining in social media / Lei Tang, Huan Liu.

Format:

Book

Author/Creator:

Tang, Lei.

Contributor:

Liu, Huan.

Series:

Synthesis lectures on data mining and knowledge discovery ; #3.

Synthesis lectures on data mining and knowledge discovery, 2151-0067 ; 3

Language:

English

Subjects (All):

Data mining--Social aspects.

Data mining.

Online social networks.

Physical Description:

1 online resource (137 p.)

Place of Publication:

[San Rafael, Calif.?] : Morgan & Claypool Publishers, 2010.

Language Note:

English

Summary:

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.

Contents:

Acknowledgments

1. Social media and social computing

Social media

Concepts and definitions

Networks and representations

Properties of large-scale networks

Challenges

Social computing tasks

Network modeling

Centrality analysis and influence modeling

Community detection

Classification and recommendation

Privacy, spam and security

Summary

2. Nodes, ties, and influence

Importance of nodes

Strengths of ties

Learning from network topology

Learning from user attributes and interactions

Learning from sequence of user activities

Influence modeling

Linear threshold model (LTM)

Independent cascade model (ICM)

Influence maximization

Distinguishing influence and correlation

3. Community detection and evaluation

Node-centric community detection

Complete mutuality

Reachability

Group-centric community detection

Network-centric community detection

Vertex similarity

Latent space models

Block model approximation

Spectral clustering

Modularity maximization

A unified process

Hierarchy-centric community detection

Divisive hierarchical clustering

Agglomerative hierarchical clustering

Community evaluation

4. Communities in heterogeneous networks

Heterogeneous networks

Multi-dimensional networks

Network integration

Utility integration

Feature integration

Partition integration

Multi-mode networks

Co-clustering on two-mode networks

Generalization to multi-mode networks

5. Social media mining

Evolution patterns in social media

A naive approach to studying community evolution

Community evolution in smoothly evolving networks

Segment-based clustering with evolving networks

Classification with network data

Collective classification

Community-based learning

A. Data collection

B. Computing betweenness

C. K-means clustering

Bibliography

Authors' biographies

Index.

Notes:

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Title from PDF t.p. (viewed on September 13, 2010).

Includes bibliographical references and index.

Cited in:

Compendex

INSPEC

Google scholar

Google book search

ISBN:

9783031019005

3031019008

9781608453559

1608453553

OCLC:

664596184