Dynamic overlapping community detection via edge-centric temporal multilayer networks

Zhaohui Li, Chenlong Wang, Biyun Han, Kexin Zhang, Xi Zhang and Liyong Yin

Chaos, Solitons & Fractals, 2026, vol. 202, issue P1

Abstract: Network analysis has emerged as a powerful methodology for characterizing the brain's complex functional states, providing quantitative frameworks to investigate brain function and associated disorders. However, conventional approaches primarily focus on constructing node-centric functional connectivity networks, often overlooking critical interactions and potential synergies among edges. This limitation constrains the representational capacity of these models and hinders the acquisition of comprehensive insights into intrinsic brain dynamics. To overcome these limitations, we propose a novel phase-difference-based edge-centric temporal multilayer network (PETMN) framework that enables more accurate estimation of dynamic network reconfiguration. Specifically, we introduce an innovative concept of edge community associated with nodes, which fundamentally challenges traditional node-based community partitioning paradigms. For performance evaluation, we apply this method to characterize epileptic brain networks across four states: interictal, and the early, mid, and late thirds of the ictal period. The results reveal significant correlations between PETMN-derived network metrics and these specific periods. Moreover, edge communities within the seizure onset zone exhibit progressive concentration from the onset time point through the temporal mid-ictal period, followed by gradual dispersion during the temporal late-ictal period. Taken together, this work offers a conceptual advance by establishing an edge-centric temporal multilayer network approach, thereby providing new perspectives for detecting dynamic overlapping communities in brain networks.

Keywords: Overlapping community detection; Edge-centric; Multilayer network; Phase difference; Epilepsy (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:202:y:2026:i:p1:s0960077925014869

DOI: 10.1016/j.chaos.2025.117473

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