Weighted layer aggregation with fast and local label expanding method for community detection in multiplex networks

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dc.authoridPouya Shahgholi / 0009-0003-6190-3529
dc.authoridAsgarali Bouyer / 0000-0002-4808-2856
dc.authoridBahman Arasteh / 0000-0001-5202-6315
dc.authoridXiaoyang Liu / 0009-0000-7821-6790
dc.authorscopusidBahman Arasteh / 39861139000
dc.authorwosidAsgarali Bouyer / IYS-5116-2023
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.contributor.authorShahgholi, Pouya
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorArasteh, Bahman
dc.contributor.authorLiu, Xiaoyang
dc.date.accessioned2025-06-03T11:54:02Z
dc.date.available2025-06-03T11:54:02Z
dc.date.issued15 Temmuz 2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractCommunity detection in multiplex networks has emerged as a crucial research area due to its ability to capture complex interactions across multiple layers of interconnected data. Despite significant advancements, existing methods often face critical challenges, including computational time, resolution limit, free parameter tuning, training models, etc. To overcome these limitations, this paper presents LCDMN (Layer-Coupled Diffusion for Multiplex Networks) algorithm designed for accurate and efficient community detection in multiplex networks. LCDMN employs dynamic scaling and layer coupling to adaptively identify community structures across diverse network configurations, offering improved resilience to network's density and structural ambiguity. LCDMN addresses the challenges of layer diversity by: (1) dynamically weighting layers based on critical parameters such as layer correlation, layer nodes activity variance, and attractiveness, (2) developing a robust node scoring method, (3) the aggregating layers of multiplex network into a single-layer, weighted graph, (4) employing a label diffusion approach with mechanisms for handling overlapping nodes, and (5) refining community structures through a dynamic merging process that adaptively adjusts layer contributions and community boundaries during execution, ensuring context-sensitive resolution of structural ambiguity. Nodes and edges are scored using network topology and structural metrics to efficiently incorporate in label diffusion process for detecting initial communities. The approach balances computational efficiency with precision, enabling the detection of cohesive and well-defined communities in complex networks. Experimental evaluations on real-world and synthetic multiplex networks demonstrate that LCDMN consistently outperforms state-of-the-art methods, such as Infomap, MDLPA, MPBTV, LART, DGFM3 and GenLouvain, in terms of Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and modularity.
dc.identifier.citationShahgholi, P., Bouyer, A., Arasteh, B., & Liu, X. (2025). Weighted Layer Aggregation with Fast and Local Label Expanding Method for Community Detection in Multiplex Networks. Physica A: Statistical Mechanics and its Applications, 130639.
dc.identifier.doi10.1016/j.physa.2025.130639
dc.identifier.endpage21
dc.identifier.issn0378-4371
dc.identifier.scopuseid=2-s2.0-105004034591
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7270
dc.identifier.volume670
dc.identifier.wosWOS:001487652500004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorBouyer, Asgarali
dc.institutionauthorArasteh, Bahman
dc.institutionauthoridAsgarali Bouyer / 0000-0002-4808-2856
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofPhysica A: Statistical Mechanics and its Applications
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCommunity detection
dc.subjectLabel diffusion
dc.subjectLayer aggregation
dc.subjectMultiplex networks
dc.subjectSimilarity
dc.titleWeighted layer aggregation with fast and local label expanding method for community detection in multiplex networks
dc.typeArticle

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