Network community detection is the science of identifying groups of vertices in complex networks, from communities of friends in social networks to functional analogues in protein interaction systems. This book offers a comprehensive exposition of network community detection, describing processes such as modularity optimization and statistical inference, and providing step-by-step treatments of the algorithms used to detect communities in temporal, multilayer, and other specific kinds of networks. The strengths and weaknesses of the primary methods are highlighted, allowing readers to identify the most suitable approach for their needs. A collection of complementary software is also freely available online, encouraging readers to experiment with a broad variety of tools. The book is well suited to graduate students and researchers in physics, computer science, mathematics, and engineering, and to those working in the social sciences and other disciplines that deal with complex networks.

Network community detection is the science of identifying groups of vertices in complex networks, from communities of friends in social networks to functional analogues in protein interaction systems. This book offers a comprehensive exposition of network community detection, describing processes such as modularity optimization and statistical inference, and providing step-by-step treatments of the algorithms used to detect communities in temporal, multilayer, and other specific kinds of networks. The strengths and weaknesses of the primary methods are highlighted, allowing readers to identify the most suitable approach for their needs. A collection of complementary software is also freely available online, encouraging readers to experiment with a broad variety of tools. The book is well suited to graduate students and researchers in physics, computer science, mathematics, and engineering, and to those working in the social sciences and other disciplines that deal with complex networks.

Network community detection is the science of identifying groups of vertices in complex networks, from communities of friends in social networks to functional analogues in protein interaction systems. This book offers a comprehensive exposition of network community detection, describing processes such as modularity optimization and statistical inference, and providing step-by-step treatments of the algorithms used to detect communities in temporal, multilayer, and other specific kinds of networks. The strengths and weaknesses of the primary methods are highlighted, allowing readers to identify the most suitable approach for their needs. A collection of complementary software is also freely available online, encouraging readers to experiment with a broad variety of tools. The book is well suited to graduate students and researchers in physics, computer science, mathematics, and engineering, and to those working in the social sciences and other disciplines that deal with complex networks.