An Efficient Framework for Computing Structure- And Semantics-Preserving Community in a Heterogeneous Multilayer Network

TL;DR

This paper presents a novel, efficient framework for detecting communities in multilayer networks that preserves the original structure and semantics, enabling detailed analysis of complex interconnected data.

Contribution

It introduces a structure-preserving community definition for MLNs and a decoupling-based framework with new algorithms and metrics for efficient community detection.

Findings

Framework effectively detects communities in MLNs

Algorithm validated on IMDb and DBLP datasets

Achieves efficient computation while preserving network semantics

Abstract

Multilayer networks or MLNs (also called multiplexes or network of networks) are being used extensively for modeling and analysis of data sets with multiple entity and feature types and associated relationships. Although the concept of community is widely-used for aggregate analysis, a structure- and semantics preserving definition for it is lacking for MLNs. Retention of original MLN structure and entity relationships is important for detailed drill-down analysis. In addition, efficient computation is also critical for large number of analysis. In this paper, we introduce a structure-preserving community definition for MLNs as well as a framework for its efficient computation using the decoupling approach. The proposed decoupling approach combines communities from individual layers to form a serial k-community for connected k layers in a MLN. We propose a new algorithm for pairing communities across layers and introduce several weight metrics for composing communities from two layers using participating community characteristics. In addition to the definition, our proposed approach has a number of desired characteristics. It: i) leverages extant single graph community detection algorithms, ii) introduces several weight metrics that are customized for the community concept, iii) is a new algorithm for pairing communities using bipartite graphs, and iv) experimentally validates the community computation and its efficiency on widely-used IMDb and DBLP data sets.