Joint Optimal Transport and Embedding for Network Alignment

• URL: http://arxiv.org/abs/2502.19334v1
• Date: Wed, 26 Feb 2025 17:28:08 GMT
• Title: Joint Optimal Transport and Embedding for Network Alignment
• Authors: Qi Yu, Zhichen Zeng, Yuchen Yan, Lei Ying, R. Srikant, Hanghang Tong,
• Abstract summary: We propose a joint optimal transport and embedding framework for network alignment named JOENA.<n>With a unified objective, the mutual benefits of both methods can be achieved by an alternating optimization schema with guaranteed convergence.<n>Experiments on real-world networks validate the effectiveness and scalability of JOENA, achieving up to 16% improvement in MRR and 20x speedup.
• Score: 66.49765320358361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network alignment, which aims to find node correspondence across different networks, is the cornerstone of various downstream multi-network and Web mining tasks. Most of the embedding-based methods indirectly model cross-network node relationships by contrasting positive and negative node pairs sampled from hand-crafted strategies, which are vulnerable to graph noises and lead to potential misalignment of nodes. Another line of work based on the optimal transport (OT) theory directly models cross-network node relationships and generates noise-reduced alignments. However, OT methods heavily rely on fixed, pre-defined cost functions that prohibit end-to-end training and are hard to generalize. In this paper, we aim to unify the embedding and OT-based methods in a mutually beneficial manner and propose a joint optimal transport and embedding framework for network alignment named JOENA. For one thing (OT for embedding), through a simple yet effective transformation, the noise-reduced OT mapping serves as an adaptive sampling strategy directly modeling all cross-network node pairs for robust embedding learning.For another (embedding for OT), on top of the learned embeddings, the OT cost can be gradually trained in an end-to-end fashion, which further enhances the alignment quality. With a unified objective, the mutual benefits of both methods can be achieved by an alternating optimization schema with guaranteed convergence. Extensive experiments on real-world networks validate the effectiveness and scalability of JOENA, achieving up to 16% improvement in MRR and 20x speedup compared with the state-of-the-art alignment methods.

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