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Graph alignment, which aims at identifying corresponding entities acrossmultiple networks, has been widely applied in various domains. As the graphs tobe aligned are usually constructed from different sources, the inconsistencyissues of structures and features between two graphs are ubiquitous inreal-world applications. Most existing methods follow the``embed-then-cross-compare'' paradigm, which computes node embeddings in eachgraph and then processes node correspondences based on cross-graph embeddingcomparison. However, we find these methods are unstable and sub-optimal whenstructure or feature inconsistency appears. To this end, we propose SLOTAlign,an unsupervised graph alignment framework that jointly performs StructureLearning and Optimal Transport Alignment. We convert graph alignment to anoptimal transport problem between two intra-graph matrices without therequirement of cross-graph comparison. We further incorporate multi-viewstructure learning to enhance graph representation power and reduce the effectof structure and feature inconsistency inherited across graphs. Moreover, analternating scheme based algorithm has been developed to address the jointoptimization problem in SLOTAlign, and the provable convergence result is alsoestablished. Finally, we conduct extensive experiments on six unsupervisedgraph alignment datasets and the DBP15K knowledge graph (KG) alignmentbenchmark dataset. The proposed SLOTAlign shows superior performance andstrongest robustness over seven unsupervised graph alignment methods and fivespecialized KG alignment methods.

Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport