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Community Structure of Complex Networks

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  • © 2013

Overview

  • Nominated by Chinese Academy of Sciences as an outstanding PhD thesis
  • A comprehensive introduction to community detection in networks
  • Includes the state-of-the-art development of community detection
  • Provides a useful complementary to complex network
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Theses (Springer Theses)

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Table of contents (5 chapters)

Keywords

About this book

Community structure is a salient structural characteristic of many real-world networks. Communities are generally hierarchical, overlapping, multi-scale and coexist with other types of structural regularities of networks. This poses major challenges for conventional methods of community detection. This book will comprehensively introduce the latest advances in community detection, especially the detection of overlapping and hierarchical community structures, the detection of multi-scale communities in heterogeneous networks, and the exploration of multiple types of structural regularities. These advances have been successfully applied to analyze large-scale online social networks, such as Facebook and Twitter. This book provides readers a convenient way to grasp the cutting edge of community detection in complex networks.
The thesis on which this book is based was honored with the “Top 100 Excellent Doctoral Dissertations Award” from the Chinese Academy of Sciences and was nominated as the “Outstanding Doctoral Dissertation” by the Chinese Computer Federation.

Reviews

From the book reviews:

“The topic of this book is the analysis of community structures. … this book provides a unique viewpoint on network analysis. It is a good handbook for engineers specializing in modern network analysis.” (Hsun-Hsien Chang, Computing Reviews, June, 2014)


“The monograph offers an exceptional set of methods of research on networks, and can be useful and interesting to researchers and students in various areas.” (Stan Lipovetsky, Technometrics, Vol. 30 (1), 2018)

Authors and Affiliations

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, People's Republic

    Hua-Wei Shen

About the author

Hua-Wei Shen is currently an associate professor at the Institute of Computing Technology, Chinese Academy of Sciences, where he leads a research group on network analysis and social computing. His main research interests include network science, recommender system, and social network analysis. He received his PhD from the Graduate University of the Chinese Academy of Sciences in 2010. His doctoral thesis was honored with the “Top 100 Excellent Doctoral Dissertations Award” by the Chinese Academy of Sciences and was nominated as the “Outstanding Doctoral Dissertation” by the Chinese Computer Federation. He has published more than 40 papers in prestigious journals and top international conferences, including PLoS ONE, Physical Review E, Journal of Statistical Mechanics, WWW, CIKM, WSDM, and IJCAI.

Bibliographic Information

  • Book Title: Community Structure of Complex Networks

  • Authors: Hua-Wei Shen

  • Series Title: Springer Theses

  • DOI: https://doi.org/10.1007/978-3-642-31821-4

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2013

  • Hardcover ISBN: 978-3-642-31820-7Published: 06 January 2013

  • Softcover ISBN: 978-3-642-43481-5Published: 24 June 2015

  • eBook ISBN: 978-3-642-31821-4Published: 06 January 2013

  • Series ISSN: 2190-5053

  • Series E-ISSN: 2190-5061

  • Edition Number: 1

  • Number of Pages: XIV, 117

  • Topics: Data Mining and Knowledge Discovery, Statistics, general

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