AIFeb 24, 2015
Tensor SimRank for Heterogeneous Information Networks
arXiv:1502.06818v13 citations
Originality Synthesis-oriented
AI Analysis
This work addresses similarity measurement for heterogeneous networks, but appears incremental as it extends an existing method.
The authors tackled the problem of measuring similarity in heterogeneous information networks by generalizing the SimRank measure, resulting in a method that computes high intraclass similarity scores when related objects are pairwise similar across all relations.
We propose a generalization of SimRank similarity measure for heterogeneous information networks. Given the information network, the intraclass similarity score s(a, b) is high if the set of objects that are related with a and the set of objects that are related with b are pair-wise similar according to all imposed relations.