Community detection in multiplex networks using locally adaptive random walks

TL;DR

This paper introduces LART, a novel community detection algorithm for multiplex networks that adapts to local layer similarities, enabling identification of communities shared by some or all layers.

Contribution

The paper presents LART, a new algorithm that uses locally adaptive random walks to detect communities in multiplex networks, accommodating partial layer sharing.

Findings

LART outperforms existing algorithms in simulated scenarios.

LART effectively detects communities shared by some or all layers.

The method adapts to local topological differences across layers.

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.