A semantic-rich similarity measure in heterogeneous information networks

Abstract Most of the existing similarity metrics in heterogeneous information networks depend on the pre-specified meta-path or meta-structure. This dependency may cause them to be sensitive to different meta-paths or meta-structures. In this paper, we propose a stratified meta-structure-based similarity measure named SMSS in heterogeneous information networks. The stratified meta-structure can be constructed automatically and capture rich semantics.Then, we define the commuting matrix of the stratified meta-structure by virtue of the commuting matrices of meta-paths and meta-structures. As a result, the SMSS is defined by virtue of this commuting matrix. Experimental evaluations show that the existing metrics are sensitive to different meta-paths or meta-structures and that the proposed SMSS outperforms the state-of-the-art metrics in terms of ranking and clustering.