{"type":"ScholarlyArticle","title":"LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity","authors":[{"name":"Yuhan Chen","slug":"yuhan-chen","given":"Yuhan","family":"Chen","affiliation":"School of Computer Science and Engineering , Guangzhou Campus of Sun Yat-sen University"},{"name":"Yihong Luo","slug":"yihong-luo","given":"Yihong","family":"Luo","affiliation":"The Hong Kong University of Science and Technology ( Guangzhou ); The Hong Kong University of Science and Technology"},{"name":"Jing Tang","slug":"jing-tang","given":"Jing","family":"Tang","affiliation":"The Hong Kong University of Science and Technology ( Guangzhou ); The Hong Kong University of Science and Technology"},{"name":"Liang Yang","slug":"liang-yang","given":"Liang","family":"Yang","affiliation":"School of Artificial Intelligence , Hebei University of Technology"},{"name":"Siya Qiu","slug":"siya-qiu","given":"Siya","family":"Qiu","affiliation":"The Hong Kong University of Science and Technology ( Guangzhou ); The Hong Kong University of Science and Technology"},{"name":"Chuan Wang","slug":"chuan-wang","given":"Chuan","family":"Wang","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences"},{"name":"Xiaochun Cao","slug":"xiaochun-cao","given":"Xiaochun","family":"Cao","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"}],"abstract":"Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion of the information of multi-hop neighbors to include more nodes with homophily. However, the heterophily might differ among nodes, which requires to consider the local topology. Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module. For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. Moreover, we provide theoretical analysis on the effectiveness of LocalSim representing node homophily on synthetic graphs. Extensive evaluations over real benchmark datasets show that our proposed method, namely Local Similarity Graph Neural Network (LSGNN), can offer comparable or superior state-of-the-art performance on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model can significantly boost the performance of existing GNNs.","doi":"10.24963/ijcai.2023/395","url":"https://ijcai.org/proceedings/2023/395","pdf":"https://ijcai.org/proceedings/2023/0395.pdf","markdown":"https://ijcai.org/proceedings/2023/395.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/395","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":395,"pages":{"first":3550,"last":3558},"keywords":["Machine Learning: ML: Sequence and graph learning","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Abu-El-Haija et al., 2019] Sami Abu-El-Haija, Bryan Per- ozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Ler- man, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Gal- styan. 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