Community Detection and Mining in Social Media

Community Detection and Mining in Social Media

Lei Tang, Huan Liu
ISBN: 9781608453542 | PDF ISBN: 9781608453559
Copyright © 2010 | 137 Pages | Publication Date: 01/01/2010

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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 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 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

About the Author(s)

Lei Tang, Yahoo! Labs
Lei Tang is a scientist at Yahoo! Labs. He received his Ph.D. in computer science and engineering at Arizona State University in 2010 and BS from Fudan University, China in 2004. His research interests include social computing, data mining, and social media mining, in particular, relational learning with heterogeneous networks, group evolution, profiling and influence modeling, and collective behavior modeling and prediction in social media. He was awarded ASU GPSA Research Grant, SDM Doctoral Student Forum Fellowship, Student Travel Awards and Scholarships in various conferences. He is a member of ACM and IEEE.

Huan Liu, Arizona State University
Huan Liu is a professor of computer science and engineering at Arizona State University (ASU). He received his Ph.D. from University of Southern California and Bachelor of Engineering from Shanghai Jiao Tong University. He has been recognized for excellence in teaching and research in the Departement of Computer Science and Engineering at ASU. His research interests include data/web mining, machine learning, social computing, and artificial intelligence, investigating problems that arise in many real-world applications with high-dimensional data of disparate forms and multiple sources such as feature selection, modeling group interaction, relational learning, text categorization, biomarker identification, and social media analysis. His well-cited publications include books, book chapters, encyclopedia entries, conference and journal papers. He serves on journal editorial boards and numerous conference program committees, and he is a founding organizer of the International Workshop/Conference Series on Social Computing, Behavioral Modeling, and Prediction (SBP).

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