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Towards effective and efficient graph alignment without supervision
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AI
DOI：10.1007/s11280-026-01411-0.png)
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Abstract
En 中文
Unsupervised graph alignment aims to find the node correspondence across different graphs without any anchor node pairs. Despite the recent efforts utilizing deep learning-based techniques, such as the embedding and optimal transport (OT)-based approaches, we observe their limitations in terms of model accuracy-efficiency tradeoff. By focusing on the exploitation of local and global graph information, we formalize them as the “local representation, global alignment” paradigm, and present a new “global representation and alignment” paradigm to resolve the mismatch between the two phases in the alignment process. We then propose Global representation and optimal transport-based Alignment (GlobAlign), and its variant, GlobAlign-E, for better Efficiency. Our methods are equipped with the global attention mechanism and a hierarchical cross-graph transport cost, able to capture long-range and implicit node dependencies beyond the local graph structure. Furthermore, GlobAlign-E successfully closes the time complexity gap between representative embedding and OT-based methods, reducing OT’s cubic complexity to quadratic terms. Through extensive experiments, our methods demonstrate superior performance, with up to a 20% accuracy improvement over the best competitor. Meanwhile, GlobAlign-E achieves the best efficiency, with an order of magnitude speedup against existing OT-based methods.
Keywords:
Unsupervised graph alignment
Alignment paradigm
Global interactions
Optimal transport
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