ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection

Abstract

Dynamic community detection is commonly addressed either by full-snapshot recomputation or by solver-specific dynamic procedures. Full recomputation preserves the semantics of mature static solvers, but it repeatedly processes unchanged graph regions when updates are small. Solver-specific dynamic methods can reduce this cost, but their update rules often have limited transferability across objectives, feature representations, and implementations. In addition, localizing computation only by graph distance may omit community context needed by high-quality solvers. We introduce ComNetX, a solver-agnostic hierarchical adaptation framework for local dynamic updates. ComNetX maintains a multi-level community state, expands the updated region, closes it over affected communities, and contracts these communities into compact local instances. This affected-community closure and contraction preserve solver context while restricting computation to the changed part of the graph. The same interface can wrap modularity heuristics, graph-clustering models that use node features, and native dynamic solvers as local backends. We evaluate ComNetX through a multi-backend study on six real networks, longer real-data streams for topology-based backends, and controlled dynamic stochastic block model stress streams. The results show that ComNetX can preserve the quality of strong modularity-based solvers while reducing update time on large graphs: in paired runs on the largest real graph, Local Leiden keeps final modularity within 0.006 of full-snapshot recomputation while achieving a 41.9 +/- 0.2x speedup. The combined protocols also identify regimes where locality breaks down and a full refresh is preferable.