| Summary: | Abstract We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering. In this paper, we provide experimental evidence to the claim that ECG alleviates the well-known resolution limit issue, and that it leads to better stability of the partitions. We propose a community strength index based on ECG results to help quantify the presence of community structure in a graph. We perform a wide range of experiments both over synthetic and real graphs, showing the usefulness of ECG over a variety of problems. In particular, we consider measures based on node partitions as well as topological structure of the communities, and we apply ECG to community-aware anomaly detection. Finally, we show that ECG can be used in a semi-supervised context to zoom in on the sub-graph most closely associated with seed nodes.
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