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Recently, there is a surge of interests on heterogeneous information networkanalysis. As a newly emerging network model, heterogeneous information networkshave many unique features (e.g., complex structure and rich semantics) and anumber of interesting data mining tasks have been exploited in this kind ofnetworks, such as similarity measure, clustering, and classification. Althoughevaluating the importance of objects has been well studied in homogeneousnetworks, it is not yet exploited in heterogeneous networks. In this paper, westudy the ranking problem in heterogeneous networks and propose the HRankframework to evaluate the importance of multiple types of objects and metapaths. Since the importance of objects depends upon the meta paths inheterogeneous networks, HRank develops a path based random walk process.Moreover, a constrained meta path is proposed to subtly capture the richsemantics in heterogeneous networks. Furthermore, HRank can simultaneouslydetermine the importance of objects and meta paths through applying the tensoranalysis. Extensive experiments on three real datasets show that HRank caneffectively evaluate the importance of objects and paths together. Moreover,the constrained meta path shows its potential on mining subtle semantics byobtaining more accurate ranking results.

HRank: A Path based Ranking Framework in Heterogeneous Information Network