Combining Optimal Transport and Embedding-Based Approaches for More Expressiveness in Unsupervised Graph Alignment

• URL: http://arxiv.org/abs/2406.13216v1
• Date: Wed, 19 Jun 2024 04:57:35 GMT
• Title: Combining Optimal Transport and Embedding-Based Approaches for More Expressiveness in Unsupervised Graph Alignment
• Authors: Songyang Chen, Yu Liu, Lei Zou, Zexuan Wang, Youfang Lin, Yuxing Chen, Anqun Pan,
• Abstract summary: Unsupervised graph alignment finds the one-to-one node correspondence between a pair of attributed graphs by only exploiting graph structure and node features. We propose a principled approach to combine their advantages motivated by theoretical analysis of model expressiveness. We are the first to guarantee the one-to-one matching constraint by reducing the problem to maximum weight matching.
• Score: 19.145556156889064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised graph alignment finds the one-to-one node correspondence between a pair of attributed graphs by only exploiting graph structure and node features. One category of existing works first computes the node representation and then matches nodes with close embeddings, which is intuitive but lacks a clear objective tailored for graph alignment in the unsupervised setting. The other category reduces the problem to optimal transport (OT) via Gromov-Wasserstein (GW) learning with a well-defined objective but leaves a large room for exploring the design of transport cost. We propose a principled approach to combine their advantages motivated by theoretical analysis of model expressiveness. By noticing the limitation of discriminative power in separating matched and unmatched node pairs, we improve the cost design of GW learning with feature transformation, which enables feature interaction across dimensions. Besides, we propose a simple yet effective embedding-based heuristic inspired by the Weisfeiler-Lehman test and add its prior knowledge to OT for more expressiveness when handling non-Euclidean data. Moreover, we are the first to guarantee the one-to-one matching constraint by reducing the problem to maximum weight matching. The algorithm design effectively combines our OT and embedding-based predictions via stacking, an ensemble learning strategy. We propose a model framework named \texttt{CombAlign} integrating all the above modules to refine node alignment progressively. Through extensive experiments, we demonstrate significant improvements in alignment accuracy compared to state-of-the-art approaches and validate the effectiveness of the proposed modules.

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