Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

TL;DR

This paper introduces a generalized framework for detecting communities in complex networks that evolve over time, have multiple types of links, and operate across multiple scales, broadening the applicability of community detection methods.

Contribution

The authors develop a unified framework of network quality functions for analyzing community structure in multislice networks, including temporal, multiplex, and multiscale networks.

Findings

Framework enables analysis of community structure in diverse network types.

Applicable to networks with evolving, multiplex, and multiscale features.

Facilitates more comprehensive understanding of complex network communities.

Abstract

Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows one to study community structure in a very general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.