A Local Perspective on Community Structure in Multilayer Networks

TL;DR

This paper explores how different random walks on multilayer networks reveal various community structures, impacting community detection methods based on these dynamics.

Contribution

It introduces a local perspective on community detection in multilayer networks by analyzing how different random walks identify distinct community bottlenecks.

Findings

Different random walks reveal diverse community structures.

Community detection methods are influenced by the type of random walk used.

Bottlenecks vary significantly across random walk types.

Abstract

The analysis of multilayer networks is among the most active areas of network science, and there are now several methods to detect dense "communities" of nodes in multilayer networks. One way to define a community is as a set of nodes that trap a diffusion-like dynamical process (usually a random walk) for a long time. In this view, communities are sets of nodes that create bottlenecks to the spreading of a dynamical process on a network. We analyze the local behavior of different random walks on multiplex networks (which are multilayer networks in which different layers correspond to different types of edges) and show that they have very different bottlenecks that hence correspond to rather different notions of what it means for a set of nodes to be a good community. This has direct implications for the behavior of community-detection methods that are based on these random walks.