Exploiting Transitive Similarity and Temporal Dynamics for Similarity Search in Heterogeneous Information Networks

摘要

Heterogeneous information networks have attracted much attention in recent years and a key challenge is to compute the similarity between two objects. In this paper, we study the problem of similarity search in heterogeneous information networks, and extend the meta path-based similarity measure PathSim by incorporating richer information, such as transitive similarity and temporal dynamics. Experiments on a large DBLP network show that our improved similarity measure is more effective at identifying similar authors in terms of their future collaborations.