{"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/large-scale-learning-on-non-homophilous","title":"Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods","arxiv_id":"2110.14446","date":"2021-10-27","proceeding":"NeurIPS 2021 12","authors":["Derek Lim","Felix Hohne","Xiuyu Li","Sijia Linda Huang","Vaishnavi Gupta","Omkar Bhalerao","Ser-Nam Lim"],"abstract":"Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. 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