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- W4313443413 abstract "Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, achieving highly precise alignment is still challenging, especially when nodes with long-range connectivity to the labeled anchors are encountered. To alleviate this limitation, we purposefully designed WL-Align which adopts 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. Data and code of WL-Align are available at https://github.com/ChenPengGang/WLAlignCode." @default.
- W4313443413 created "2023-01-06" @default.
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- W4313443413 date "2022-12-29" @default.
- W4313443413 modified "2023-09-23" @default.
- W4313443413 title "WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning" @default.
- W4313443413 doi "https://doi.org/10.48550/arxiv.2212.14182" @default.
- W4313443413 hasPublicationYear "2022" @default.
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