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A novel robust integrating method by high-order proximity for self-supervised attribute network embedding
Zelong Wu, Yidan Wang, Kaixia Hu, Guoliang Lin, Xinwei Xu
Attribute network embedding faces significant challenges, primarily integrating heterogeneous information and managing outliers. In this paper, we introduce a novel Robust Integrating Method by High-order Proximity for Self-supervised Attribute Network Embedding (RSANE). Firstly, a novel heterogeneous topological and semantic information integration method is designed, which contains arbitrary high-order proximity and theoretically includes summation and multiplication forms. Secondly, the RSANE can adaptively reduce the influence of outliers during the embedding process. By incorporating higher-order proximity, RSANE increases the score of outliers and achieves better robustness. Finally, through a deep architecture with dual autoencoders, RSANE achieves joint embedding of network structure and node attributes. In addition, introducing end-to-end reconstruction of structure and attributes can fully extract potential information. Moreover, extensive experiments associated with statistical tests and sensitivity analysis demonstrate that RSANE outperforms state-of-the-art algorithms across various downstream tasks. The code is available at https://github.com/wuzelong/RSANE.
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