2009 RankingbasedClusteringofHeterog

Jump to navigation Jump to search

Subject Headings:


Cited By


Author Keywords

Heterogeneous Information Network, Clustering


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 multi-typed 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.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 RankingbasedClusteringofHeterogYizhou Sun
Yintao Yu
Jiawei Han
Ranking-based Clustering of Heterogeneous Information Networks with Star Network SchemaKDD-2009 Proceedingshttp://www.cs.uiuc.edu/~hanj/pdf/kdd09 ysun.pdf10.1145/1557019.15571072009