{"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/combating-bilateral-edge-noise-for-robust-1","title":"Combating Bilateral Edge Noise for Robust Link Prediction","arxiv_id":"2311.01196","date":"2023-11-02","proceeding":"NeurIPS 2023 11","authors":["Zhanke Zhou","Jiangchao Yao","Jiaxu Liu","Xiawei Guo","Quanming Yao","Li He","Liang Wang","Bo Zheng","Bo Han"],"abstract":"Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less investigated. 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