Meta-Path Reduction With Transition Probability Preserving In Heterogeneous Information Network

Heterogeneous Information Network (HIN) has attracted much attention due to its wide applicability in a variety of data mining tasks. A potentially large number of meta-paths can be extracted from the heterogeneous networks, providing abundant semantic knowledge. However, too many meta-paths may be redundant. Reduction on the number of meta-paths can enhance the effectiveness since some redundant meta-paths provide interferential linkage to the task. Moreover, the reduced meta-paths can reflect the characteristic of the heterogeneous network. In this paper, unlike previous supervised model, we propose a novel algorithm, SPMR (Semantic Preserving Metapath Reduction), to reduce a set of pre-defined meta-paths in an unsupervised setting. The proposed method is able to evaluate a set of meta-paths to maximally preserve the semantics of original meta-paths after reduction. Experimental results show that SPMR can select a succinct subset of meta-paths which can achieve comparable or even better performance with fewer meta-paths.