2015/07/06 by Zhana Kuncheva, Kuncheva, Zhana, Giovanni Montana +1
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Network Security and Intrusion Detection #Opinion Dynamics and Social Influence #Peer-to-Peer Network Technologies #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1507.01890
openalex publication_date 2015/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multiplex networks, a special type of multilayer networks, are increasingly\napplied in many domains ranging from social media analytics to biology. A\ncommon task in these applications concerns the detection of community\nstructures. Many existing algorithms for community detection in multiplexes\nattempt to detect communities which are shared by all layers. In this article\nwe propose a community detection algorithm, LART (Locally Adaptive Random\nTransitions), for the detection of communities that are shared by either some\nor all the layers in the multiplex. The algorithm is based on a random walk on\nthe multiplex, and the transition probabilities defining the random walk are\nallowed to depend on the local topological similarity between layers at any\ngiven node so as to facilitate the exploration of communities across layers.\nBased on this random walk, a node dissimilarity measure is derived and nodes\nare clustered based on this distance in a hierarchical fashion. We present\nexperimental results using networks simulated under various scenarios to\nshowcase the performance of LART in comparison to related community detection\nalgorithms.\n