Ensemble Clustering for Graphs
This work addresses graph clustering for researchers, but it is incremental as it builds on existing methods like Louvain and consensus clustering.
The authors tackled the problem of graph clustering by proposing an ensemble clustering algorithm (ECG) based on the Louvain algorithm and consensus clustering, and they showed that ECG outperforms leading algorithms in a replicated study on artificial networks.
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.