WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users Across Networks via Regularized Representation Learning

Li Liu, Penggang Chen, Xin Li*, William K. Cheung*, Youmin Zhang, Qun Liu, Guoyin Wang

*此作品的通讯作者

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摘要

Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, highly precise alignment remains challenging, especially for nodes with long-range connectivity to labeled anchors. To alleviate this limitation, we propose WL-Align which employs a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of 'across-network Weisfeiler-Lehman relabeling' and 'proximity-preserving representation learning'. The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors' connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the 'exact matching' scenario.

源语言英语
页(从-至)445-458
页数14
期刊IEEE Transactions on Knowledge and Data Engineering
36
1
DOI
出版状态已出版 - 1 1月 2024

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引用此

Liu, L., Chen, P., Li, X., Cheung, W. K., Zhang, Y., Liu, Q., & Wang, G. (2024). WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users Across Networks via Regularized Representation Learning. IEEE Transactions on Knowledge and Data Engineering, 36(1), 445-458. https://doi.org/10.1109/TKDE.2023.3277843