Community detection in multiplex networks using locally adaptive random walks
This addresses community detection in multiplex networks for applications like social media analytics and biology, but it is incremental as it builds on existing random walk methods.
The authors tackled the problem of detecting communities in multiplex networks that are shared by some or all layers, proposing the LART algorithm based on locally adaptive random walks, and showed its performance in simulations compared to related methods.
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.