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Ranking-Based Clustering of Heterogeneous Information Networks with Star Network Schema



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Ranking Based Clustering of Heterogeneous Information Networks with Star Network Schema Yizhou Sun Yintao Yu Jiawei Han Department of Computer Science UIUC sun22 uiuc edu yintao uiuc edu hanj cs uiuc edu ABSTRACT 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 Categories and Subject Descriptors H 2 8 Information Systems Applications Database Applications Data Mining The work was supported in part by the U S National Science Foundation grants IIS 08 42769 and BDI 05 15813 Office of Naval Research ONR grant N00014 08 1 0565 and the Air Force Office of Scientific Research MURI award FA9550 08 1 0265 Any opinions findings and conclusions expressed here are



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