Hierarchical Community Detection in Bipartite Networks
2026-04-09 • Social and Information Networks
Social and Information Networks
AI summaryⓘ
The authors created a new method called Qbg to find nested groups (hierarchies) in networks with two different types of nodes, called bipartite networks. Their method works well even when the connections have weights and lets users adjust the detail level to see bigger or smaller groups. Unlike older methods, it keeps the original network shape without simplifying it. They tested Qbg on fake and real networks, finding it not only spots known groups but also uncovers smaller and layered groups other methods miss.
bipartite networkscommunity detectionhierarchical structuremodularityresolution parameterweighted networksmesoscale structurenetwork topologysynthetic benchmarknetwork projection
Authors
Tania Ghosh, Kevin E. Bassler
Abstract
Many bipartite networks exhibit hierarchical community structure, but existing community detection methods are not well-suited for detecting hierarchy. They also do not effectively handle weighted bipartite networks. In this work, we introduce a novel modularity-based objective function, called the generalized bipartite modularity density, Qbg, specifically designed for hierarchical community detection in bipartite systems. The framework incorporates a tunable resolution parameter that enables systematic exploration of community structure across multiple scales. It leverages resolution-limit behavior in bipartite networks as a tool to uncover hierarchical organization without projecting the network or altering its intrinsic bipartite topology. We evaluate the method using a hierarchical synthetic bipartite benchmark and apply it to two empirical networks. In all cases, Qbg recovers established mesoscale structure while revealing additional hierarchical and fine-scale organization beyond that detected by conventional bipartite approaches. These results establish Qbg as a flexible, interpretable, and resolution-aware framework for hierarchical community detection in bipartite networks.