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Community Detection and Mining in Social Media

Morgan & Claypool Synthesis Lectures on Data Mining and Knowledge Discovery
2010, 137 pages, (doi:10.2200/S00298ED1V01Y201009DMK003)

The ebook is available from the following link:
http://www.morganclaypool.com/doi/abs/10.2200/S00298ED1V01Y201009DMK003

Lecture slides and supporting materials are also available:
http://dmml.asu.edu/cdm/

Authors:
Lei Tang, Yahoo! Labs
Huan Liu, Arizona State University


Abstract
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.

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 for
the latest information.


Table of Contents:
1. Social Media and Social Computing
2. Nodes, Ties, and Influence
3. Community Detection and Evaluation
4. Communities in Heterogeneous Networks
5. Social Media Mining


--
Lei Tang
Scientist, Yahoo! Labs
http://www.public.asu.edu/~ltang9/

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