Consensus Community Detection in Multilayer Networks using Parameter-free Graph Pruning

TL;DR

This paper introduces a parameter-free, ensemble-based framework for multilayer community detection that improves robustness by filtering noisy associations and optimizing modularity, outperforming existing methods.

Contribution

The work presents a novel parameter-free graph pruning approach for consensus community detection in multilayer networks, enhancing robustness and accuracy.

Findings

Model-based filtering improves community detection accuracy.

The proposed method outperforms state-of-the-art algorithms.

Filtering noisy co-associations enhances robustness.

Abstract

The clustering ensemble paradigm has emerged as an effective tool for community detection in multilayer networks, which allows for producing consensus solutions that are designed to be more robust to the algorithmic selection and configuration bias. However, one limitation is related to the dependency on a co-association threshold that controls the degree of consensus in the community structure solution. The goal of this work is to overcome this limitation with a new framework of ensemble-based multilayer community detection, which features parameter-free identification of consensus communities based on generative models of graph pruning that are able to filter out noisy co-associations. We also present an enhanced version of the modularity-driven ensemble-based multilayer community detection method, in which community memberships of nodes are reconsidered to optimize the multilayer modularity of the consensus solution. Experimental evidence on real-world networks confirms the beneficial effect of using model-based filtering methods and also shows the superiority of the proposed method on state-of-the-art multilayer community detection.