GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs

TL;DR

GSim introduces a novel GNN-based relevance measure for heterogeneous graphs that automatically captures semantic information without relying on predefined meta-paths, outperforming existing methods.

Contribution

The paper proposes GSim, a new GNN-based relevance measure that automatically leverages semantic context in heterogeneous graphs, eliminating the need for domain-specific meta-paths.

Findings

GSim outperforms existing relevance measures in experiments.

Theoretical analysis confirms GNN's effectiveness in relevance measurement.

CP-GNN effectively captures semantics in heterogeneous graphs.

Abstract

Heterogeneous graphs, which contain nodes and edges of multiple types, are prevalent in various domains, including bibliographic networks, social media, and knowledge graphs. As a fundamental task in analyzing heterogeneous graphs, relevance measure aims to calculate the relevance between two objects of different types, which has been used in many applications such as web search, recommendation, and community detection. Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path. Defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. Recently, the Graph Neural Network (GNN) has been widely applied in many graph mining tasks, but it has not been applied for measuring relevance yet. To address the aforementioned problems, we propose a novel GNN-based relevance measure, namely GSim. Specifically, we first theoretically analyze and show that GNN is effective for measuring the relevance of nodes in the graph. We then propose a context path-based graph neural network (CP-GNN) to automatically leverage the semantics in heterogeneous graphs. Moreover, we exploit CP-GNN to support relevance measures between two objects of any type. Extensive experiments demonstrate that GSim outperforms existing measures.