A novel robust integrating method by high-order proximity for self-supervised attribute network embedding

원문 초록

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.