Efficient Community Detection in Boolean Composed Multiplex Networks

TL;DR

This paper introduces a network decomposition method for efficiently detecting communities in multilayer networks by analyzing layers individually and aggregating results, significantly reducing computation time while maintaining high accuracy.

Contribution

It presents a novel network decomposition approach that improves efficiency in multilayer community detection compared to traditional methods.

Findings

Reduces computation time significantly

Maintains high accuracy in community detection

Effective on real-world and synthetic datasets

Abstract

Networks (or graphs) are used to model the dyadic relations between entities in a complex system. In cases where there exists multiple relations between the entities, the complex system can be represented as a multilayer network, where the network in each layer represents one particular relation (or feature). The analysis of multilayer networks involves combining edges from specific layers and then computing a network property. Different subsets of the layers can be combined. For any Boolean combination operation (e.g. AND, OR), the number of possible subsets is exponential to the number of layers. Thus recomputing for each subset from scratch is an expensive process. In this paper, we propose to efficiently analyze multilayer networks using a method that we term network decomposition. Network decomposition is based on analyzing each network layer individually and then aggregating the analysis results. We demonstrate the effectiveness of using network decomposition for detecting communities on different combinations of network layers. Our results on multilayer networks obtained from real-world and synthetic datasets show that our proposed network decomposition method requires significantly lower computation time while producing results of high accuracy.