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“Two‑mode networks” (also called bipartite or affiliation networks) is a book category focused on networks that link two distinct kinds of entities, with ties only across types—such as people-to-events, authors-to-papers, users-to-items, species-to-habitats, or firms-to-boards. Books in this area cover conceptual foundations, data structures (incidence matrices), and methods tailored to two‑mode data, including projection techniques and their pitfalls, centrality and clustering for bipartite graphs, blockmodeling, and statistical models (e.g., ERGMs and stochastic block models) for affiliation structures. The category spans primers, advanced monographs, handbooks, applied case studies across social science, ecology, economics, and information science, as well as practical guides to software and workflows (e.g., R, Python, UCINET, Gephi). Typical themes include measuring co‑occurrence, weighting and inference in projections, visualization of layered relations, and dynamics of evolving two‑mode systems. It is aimed at researchers, analysts, and practitioners who study or leverage relationships between two classes of actors or items.

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