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Fast Algorithm for Integrating Clustering with Ranking on Heterogeneous Graphs

The demands for graph data analysis methods, e.g., clustering and ranking, are increasing. RankClus is a framework to extract good clusters by integrating clustering and ranking on heterogeneous graphs; it enhances the clustering results by alternately updates the results of clustering and ranking for the better understanding of the clusters. However, RankClus is computationally expensive if a graph is large since it needs to iterate both clustering and ranking for all nodes. In this paper, to address this problem, we propose a novel fast RankClus algorithm for heterogeneous graphs. To speed up the entire procedure of RankClus, our proposed algorithm reduces the computational cost of the ranking process in each iteration. Our proposal measures how each node affects the clustering result; if it is not significant, we prune the node. Our extensive evaluations showed that our proposal is much faster than the original method.