Recurrent Meta-Structure for Robust Similarity Measure in Heterogeneous Information Networks

TL;DR

This paper introduces RMSS, a new similarity measure for heterogeneous information networks that automatically constructs a recurrent meta-structure, making it robust and outperforming existing metrics in ranking and clustering tasks.

Contribution

The paper proposes a novel recurrent meta-structure-based similarity measure that automatically integrates meta-paths and meta-structures, enhancing robustness in heterogeneous network analysis.

Findings

RMSS outperforms existing similarity metrics in ranking tasks.

RMSS is robust to different meta-paths and meta-structures.

Experimental results confirm the effectiveness of RMSS in clustering.

Abstract

Similarity measure as a fundamental task in heterogeneous information network analysis has been applied to many areas, e.g., product recommendation, clustering and Web search. Most of the existing metrics depend on the meta-path or meta-structure specified by users in advance. These metrics are thus sensitive to the pre-specified meta-path or meta-structure. In this paper, a novel similarity measure in heterogeneous information networks, called Recurrent Meta-Structure-based Similarity (RMSS), is proposed. The recurrent meta-structure as a schematic structure in heterogeneous information networks provides a unified framework to integrate all of the meta-paths and meta-structures. Therefore, RMSS is robust to the meta-paths and meta-structures. We devise an approach to automatically constructing the recurrent meta-structure. In order to formalize the semantics, the recurrent meta-structure is decomposed into several recurrent meta-paths and recurrent meta-trees, and we then define the commuting matrices of the recurrent meta-paths and meta-trees. All of the commuting matrices of the recurrent meta-paths and meta-trees are combined according to different weights. Note that the weights can be determined by two kinds of weighting strategies: local weighting strategy and global weighting strategy. As a result, RMSS is defined by virtue of the final commuting matrix. Experimental evaluations show that the existing metrics are sensitive to different meta-paths or meta-structures and that the proposed RMSS outperforms the existing metrics in terms of ranking and clustering tasks.