QGraph: A Quality Assessment Index for Graph Clustering

In this work, we aim to study the cluster validity problem for graph data. We present a new validity index that evaluates structural characteristics of graphs in order to select the clusters that best represent the communities in a graph. Since the work of defining what constitutes cluster in a graph is rather difficult, we exploit concepts of graph theory in order to evaluate the cohesiveness and separation of nodes. More specifically, we use the concept of degeneracy, and graph density to evaluate the connectivity of nodes in and between clusters. The effectiveness of our approach is experimentally evaluated using real-world data collections.