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 wasnominated as the "Outstanding Doctoral Dissertation" by the Chinese Computer Federation.
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 wasnominated as the "Outstanding Doctoral Dissertation" by the Chinese Computer Federation.
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)
"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)