# Ensemble Clustering for Graphs: Comparisons and Applications

## TL;DR

This paper evaluates an ensemble clustering algorithm for graphs, demonstrating its advantages in stability, resolution limit mitigation, and community detection, with broad applications across weighted graphs and anomaly detection.

## Contribution

It extends previous work by comparing ECG across diverse graph parameters, showing improved stability and community detection capabilities.

## Key findings

- ECG outperforms leading algorithms on benchmark graphs.
- ECG alleviates the resolution limit issue.
- ECG enhances community detection and anomaly detection.

## Abstract

We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering. We validated our approach by replicating a study comparing graph clustering algorithms over benchmark graphs, showing that ECG outperforms the leading algorithms. In this paper, we extend our comparison by considering a wider range of parameters for the benchmark, generating graphs with different properties. We provide new experimental results showing that the ECG algorithm alleviates the well-known resolution limit issue, and that it leads to better stability of the partitions. We also illustrate how the ensemble obtained with ECG can be used to quantify the presence of community structure in the graph, and to zoom in on the sub-graph most closely associated with seed vertices. Finally, we illustrate further applications of ECG by comparing it to previous results for community detection on weighted graphs, and community-aware anomaly detection.