NDLI: Community Detection in Multiplex Networks using Locally Adaptive Random Walks

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Community Detection in Multiplex Networks using Locally Adaptive Random Walks

Content Provider	ACM Digital Library
Author	Kuncheva, Zhana Montana, Giovanni
Abstract	Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex. The algorithm is based on a random walk on the multiplex, and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers. Based on this random walk, a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion. We present experimental results using networks simulated under various scenarios to showcase the performance of LART in comparison to related community detection algorithms.
Starting Page	1308
Ending Page	1315
Page Count	8
File Format	PDF
ISBN	9781450338547
DOI	10.1145/2808797.2808852
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-25
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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