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Boosting SimRank with Semantics

The problem of estimating the similarity of a pair of nodes in an information network draws extensive interest in numerous fields, e.g., social networks and recommender systems. In this work we revisit SimRank, a popular andwell studied similarity measure for information networks, that quantifies the similarity of two nodes based on the similarity of their neighbors. SimRank’s popularity stems from its simple, declarative definition and its efficient, scalable computation. However, despite its wide adaptation, it has been observed that for many applications SimRank may yield inaccurate similarity estimations, due to the fact that it focuses on the network structure and ignores the semantics conveyed in the node/edge labels. Therefore, the question that we ask is can SimRank be enriched with semantics while preserving its