document: Ranking-based clustering of heterogeneous information networks with star network schema A heterogeneous information network is an information network composed of multiple types of objects . Clustering on such a network may lead to better understanding of both hidden structures of the network and the individual role played by every object in each cluster . However , although clustering on homogeneous networks has been studied over decades , clustering on heterogeneous networks has not been addressed until recently . A recent study proposed a new algorithm , RankClus , for clustering on bi-typed heterogeneous networks . However , a real-world network may consist of more than two types , and the interactions among multi-typed objects play a key role at disclosing the rich semantics that a network carries . In this paper , we study clustering of multi-typed heterogeneous networks with a star network schema and propose a novel algorithm , NetClus , that utilizes links across multityped objects to generate high-quality net-clusters . An iterative enhancement method is developed that leads to effective ranking-based clustering in such heterogeneous networks . Our experiments on DBLP data show that NetClus generates more accurate clustering results than the baseline topic model algorithm PLSA and the recently proposed algorithm , RankClus . Further , NetClus generates informative clusters , presenting good ranking and cluster membership information for each attribute object in each net-cluster .
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