{"type":"ScholarlyArticle","title":"Imbalanced Node Classification Beyond Homophilic Assumption","authors":[{"name":"Jie Liu","slug":"jie-liu","given":"Jie","family":"Liu","affiliation":"Northwestern Polytechnical University"},{"name":"Mengting He","slug":"mengting-he","given":"Mengting","family":"He","affiliation":"Northwestern Polytechnical University"},{"name":"Guangtao Wang","slug":"guangtao-wang","given":"Guangtao","family":"Wang","affiliation":"Bytedance Inc"},{"name":"Quoc Viet Hung Nguyen","slug":"quoc-viet-hung-nguyen","given":"Quoc Viet Hung","family":"Nguyen","affiliation":"Griffith University"},{"name":"Xuequn Shang","slug":"xuequn-shang","given":"Xuequn","family":"Shang","affiliation":"Northwestern Polytechnical University"},{"name":"Hongzhi Yin","slug":"hongzhi-yin","given":"Hongzhi","family":"Yin","affiliation":"The University of Queensland"}],"abstract":"Imbalanced node classification widely exists in real-world networks where graph neural networks (GNNs) are usually highly inclined to majority classes and suffer from severe performance degradation on classifying minority class nodes. Various imbalanced node classification methods have been proposed recently which construct synthetic nodes and edges w.r.t. minority classes to balance the label/topology distribution. However, they are all based on homophilic assumption that nodes of the same label tend to connect despite the widely existence of heterophilic edges in real-world graphs. Thus, they uniformly aggregate features from both homophilic and heterophilic neighbors and rely on feature similarity to generate synthetic edges, which cannot be applied to imbalanced graphs in high heterophily. To address this problem, we propose a novel GraphSANN for imbalanced node classification on both homophilic and heterophilic graphs. Firstly, we propose a unified feature mixer to generate synthetic nodes with both homophilic and heterophilic interpolation in a unified way. Next, by randomly sampling edges between synthetic nodes and existing nodes as candidata edges, we design an adaptive subgraph extractor to dynamically extract the contextual subgraphs of candidate edges with flexible ranges. Finally, we develop a multi-filter subgraph encoder which constructs multiple different filter channels to discriminatively aggregate neighbors’ information along the homophilic and heterophilic edges. Extensive experiments on eight benchmark datasets demonstrate the superiority of our model for imbalanced node classificaiton on both homophilic and heterophilic graphs.","doi":"10.24963/ijcai.2023/848","url":"https://ijcai.org/proceedings/2023/848","pdf":"https://ijcai.org/proceedings/2023/0848.pdf","markdown":"https://ijcai.org/proceedings/2023/848.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/848","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":"Late Papers","track":"Main Track","number":848,"pages":{"first":7206,"last":7214},"keywords":["Machine Learning: ML: Learning graphical models","Data Mining: DM: Class imbalance and unequal cost","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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