HRank: A Path based Ranking Framework in Heterogeneous Information Network
This work addresses ranking in heterogeneous networks, a domain-specific problem for data mining researchers, but it appears incremental as it adapts ranking concepts from homogeneous networks.
The authors tackled the problem of ranking objects in heterogeneous information networks, which had not been previously exploited, by proposing the HRank framework that evaluates the importance of multiple types of objects and meta paths through a path-based random walk process and tensor analysis, with experiments on three real datasets showing it effectively evaluates importance together.
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.