Ensemble Clustering for Graphs

TL;DR

This paper introduces ECG, an ensemble clustering algorithm for graphs that improves community detection accuracy and provides a way to quantify community structure, validated through empirical comparisons.

Contribution

The paper presents ECG, a novel ensemble clustering method for graphs that outperforms existing algorithms and offers community structure quantification.

Findings

ECG outperforms leading graph clustering algorithms in experiments.

Ensemble clustering with ECG enhances community detection accuracy.

ECG can quantify the strength of community structures.

Abstract

We propose an ensemble clustering algorithm for graphs (ECG), which is based on the Louvain algorithm and the concept of consensus clustering. We validate our approach by replicating a recently published study comparing graph clustering algorithms over artificial networks, showing that ECG outperforms the leading algorithms from that study. We also illustrate how the ensemble obtained with ECG can be used to quantify the presence of community structure in the graph.