Abstract
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.
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He, J., Bailey, J., Zhang, R. (2014). Exploiting Transitive Similarity and Temporal Dynamics for Similarity Search in Heterogeneous Information Networks. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8422. Springer, Cham. https://doi.org/10.1007/978-3-319-05813-9_10
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DOI: https://doi.org/10.1007/978-3-319-05813-9_10
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