Towards Effective and Efficient Graph Alignment without Supervision

2026-03-09

AI Summary

This work addresses the challenge of accurately and efficiently establishing node correspondences in unsupervised graph alignment by proposing a novel "global representation and alignment" paradigm. Departing from the conventional decoupled framework of "local representation, global alignment," the method unifies representation learning and alignment into a single integrated process. It leverages a global attention mechanism combined with hierarchical cross-graph optimal transport to achieve anchor-free alignment. Furthermore, an efficient variant, GlobAlign-E, is introduced, reducing the computational complexity of optimal transport from cubic to quadratic. Experimental results demonstrate that the proposed approach improves alignment accuracy by up to 20% and accelerates computation by an order of magnitude compared to existing optimal transport-based methods, offering significant advantages in both precision and efficiency.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Local Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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 \underline{Gl}obal representation and \underline{o}ptimal transport-\underline{b}ased \underline{Align}ment (\texttt{GlobAlign}), and its variant, \texttt{GlobAlign-E}, for better \underline{E}fficiency. 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, \texttt{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, \texttt{GlobAlign-E} achieves the best efficiency, with an order of magnitude speedup against existing OT-based methods.

Problem

• unsupervised graph alignment
• node correspondence
• accuracy-efficiency tradeoff
• optimal transport
• graph representation

Innovation

• graph alignment
• unsupervised learning
• optimal transport
• global attention
• time complexity reduction