  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.
