{"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/generalizing-graph-neural-networks-beyond","title":"Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs","arxiv_id":"2006.11468","date":"2020-06-20","proceeding":"NeurIPS 2020 12","authors":["Jiong Zhu","Yujun Yan","Lingxiao Zhao","Mark Heimann","Leman Akoglu","Danai Koutra"],"abstract":"We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.e., in networks where connected nodes may have different class labels and dissimilar features. 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