{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-novel-robust-integrating-method-by-high","title":"A novel robust integrating method by high-order proximity for self-supervised attribute network embedding","arxiv_id":null,"date":"2024-12-10","proceeding":"Expert Systems with Applications 2024 12","authors":["Zelong Wu","Yidan Wang","Kaixia Hu","Guoliang Lin","Xinwei Xu"],"abstract":"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.","url_abs":"https://doi.org/10.1016/j.eswa.2024.125911","url_pdf":"https://doi.org/10.1016/j.eswa.2024.125911","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-novel-robust-integrating-method-by-high","repo_url":"https://github.com/wuzelong/RSANE","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"data-visualization","task_name":"Data Visualization"},{"task_slug":"graph-outlier-detection","task_name":"Graph Outlier Detection"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"network-embedding","task_name":"Network Embedding"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}