HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity

• URL: http://arxiv.org/abs/2503.08739v1
• Date: Tue, 11 Mar 2025 07:36:35 GMT
• Title: HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
• Authors: Shilong Sang, Ke-Jia Chen, Zheng liu,
• Abstract summary: This paper proposes a Heterogeneous Graph Matching Network (HeGMN)<n>It is an end-to-end graph similarity learning framework composed of a two-tier matching mechanism.<n>HeGMN consistently achieves advanced performance on graph similarity prediction across all datasets.
• Score: 3.6560264185068916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph similarity learning (GSL), also referred to as graph matching in many scenarios, is a fundamental problem in computer vision, pattern recognition, and graph learning. However, previous GSL methods assume that graphs are homogeneous and struggle to maintain their performance on heterogeneous graphs. To address this problem, this paper proposes a Heterogeneous Graph Matching Network (HeGMN), which is an end-to-end graph similarity learning framework composed of a two-tier matching mechanism. Firstly, a heterogeneous graph isomorphism network is proposed as the encoder, which reinvents graph isomorphism network for heterogeneous graphs by perceiving different semantic relationships during aggregation. Secondly, a graph-level and node-level matching modules are designed, both employing type-aligned matching principles. The former conducts graph-level matching by node type alignment, and the latter computes the interactions between the cross-graph nodes with the same type thus reducing noise interference and computational overhead. Finally, the graph-level and node-level matching features are combined and fed into fully connected layers for predicting graph similarity scores. In experiments, we propose a heterogeneous graph resampling method to construct heterogeneous graph pairs and define the corresponding heterogeneous graph edit distance, filling the gap in missing datasets. Extensive experiments demonstrate that HeGMN consistently achieves advanced performance on graph similarity prediction across all datasets.

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