  Recently, there is a surge of interests on heterogeneous information network
analysis. As a newly emerging network model, heterogeneous information networks
have many unique features (e.g., complex structure and rich semantics) and a
number of interesting data mining tasks have been exploited in this kind of
networks, such as similarity measure, clustering, and classification. Although
evaluating the importance of objects has been well studied in homogeneous
networks, it is not yet exploited in heterogeneous networks. In this paper, we
study the ranking problem in heterogeneous networks and propose the HRank
framework to evaluate the importance of multiple types of objects and meta
paths. Since the importance of objects depends upon the meta paths in
heterogeneous networks, HRank develops a path based random walk process.
Moreover, a constrained meta path is proposed to subtly capture the rich
semantics in heterogeneous networks. Furthermore, HRank can simultaneously
determine the importance of objects and meta paths through applying the tensor
analysis. Extensive experiments on three real datasets show that HRank can
effectively evaluate the importance of objects and paths together. Moreover,
the constrained meta path shows its potential on mining subtle semantics by
obtaining more accurate ranking results.
