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. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
He, J., Bailey, J., & Zhang, R. (2014). Exploiting transitive similarity and temporal dynamics for similarity search in heterogeneous information networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8422 LNCS, pp. 141–155). Springer Verlag. https://doi.org/10.1007/978-3-319-05813-9_10
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