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Community detection is a book category focused on the theory and practice of uncovering groups of tightly connected entities within complex networks, such as social, biological, technological, and information systems. Titles in this genre span foundational graph and network science, algorithmic approaches (e.g., modularity optimization, spectral and hierarchical methods, stochastic block models), overlapping and dynamic communities, and evaluation and benchmarking. Many books blend theory with hands-on guidance on scalable computation, visualization, and software tools, illustrated through case studies from areas like social media, epidemiology, neuroscience, ecology, marketing, and cybersecurity. Aimed at researchers, data scientists, and students, the category emphasizes turning network data into interpretable structure for analysis, prediction, and decision-making, while addressing challenges like noise, sparsity, scalability, and ethical data use.

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