Explicit objective functions in modularity-based community detection on multiplex networks

Abstract

Early, simultaneous, and late fusion combine network layers at different stages of community detection. Their modularity values consequently need not measure the same property of a partition. For normalized shared-node multiplexes, a unified comparison extends an existing two-layer identity: early fusion equals simultaneous fusion plus a weighted variance of community degree volumes. An elementary simplex argument explains simultaneous fusion's preference for single-layer weights. For a specified late-fusion construction, an additional transformation correction separates changes to the graph from changes to the objective. Exact small-network experiments and Louvain and Leiden experiments illustrate these distinctions. On label-free Cora, all objective-selected weights favor citations alone, while the fixed equal-weight mixtures achieve higher median adjusted Rand agreement with subject categories. The analysis provides a diagnostic for interpreting fusion objectives; higher objective values alone do not establish better recovery of externally defined communities.

arXiv commentRevised exposition of the fusion-objective relationships; added label-free Cora external validation, fixed-weight baselines, and a reproducibility supplement. Code and reproduction materials: https://gitlab.com/aipetr/modularity_objective_functions