A discussion of community structure in networks and methods for its detection. The chapter begins with an introduction to the idea of community structure, followed by descriptions of a range of methods for finding communities, including modularity maximization, the InfoMap method, methods based on maximum-likelihood fits of models to network data, betweenness-based methods, and hierarchical clustering. Also discussed are methods for assessing algorithm performance, along with a summary of performance studies and their findings. The chapter concludes with a discussion of other types of large-scale structure in networks, such as overlapping and hierarchical communities, core-periphery structure, latent-space structure, and rank structure.
Keywords: Community structure, community detection, modularity, modularity maximization, spectral algorithm, Kernighan-Lin algorithm, Louvain algorithm, resolution limit, information theory, InfoMap, statistical inference, stochastic block model, maximum likelihood, betweenness, dendrogram, hierarchical clustering, normalized mutual information, Rand index, LFR benchmark, karate club network, hierarchical structure, core-periphery structure, latent space, stratified network
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