{"type":"ScholarlyArticle","title":"HeterGCL: Graph Contrastive Learning Framework on Heterophilic Graph","authors":[{"name":"Chenhao Wang","slug":"chenhao-wang","given":"Chenhao","family":"Wang","affiliation":"Heilongjiang University"},{"name":"Yong Liu","slug":"yong-liu","given":"Yong","family":"Liu","affiliation":"Heilongjiang University"},{"name":"Yan Yang","slug":"yan-yang","given":"Yan","family":"Yang","affiliation":"Heilongjiang University"},{"name":"Wei Li","slug":"wei-li","given":"Wei","family":"Li","affiliation":"Harbin Engineering University"}],"abstract":"Graph Contrastive Learning (GCL) has attracted significant research attention due to its self-supervised ability to learn robust node representations. Unfortunately, most methods primarily focus on homophilic graphs, rendering them less effective for heterophilic graphs. In addition, the complexity of node interactions in heterophilic graphs poses considerable challenges to augmentation schemes, coding architectures, and contrastive designs for traditional GCL. In this work, we propose HeterGCL, a novel graph contrastive learning framework with structural and semantic learning to explore the true potential of GCL on heterophilic graphs. Specifically, We abandon the random augmentation scheme that leads to the destruction of the graph structure, instead introduce an adaptive neighbor aggregation strategy (ANA) to extract topology-supervised signals from neighboring nodes at different distances and explore the structural information with an adaptive local-to-global contrastive loss. In the semantic learning module, we jointly consider the original nodes' features and the similarity between nodes in the latent feature space to explore hidden associations between nodes. Experimental results on homophilic and heterophilic graphs demonstrate that HeterGCL outperforms existing self-supervised and semi-supervised baselines across various downstream tasks.","doi":"10.24963/ijcai.2024/265","url":"https://ijcai.org/proceedings/2024/265","pdf":"https://ijcai.org/proceedings/2024/0265.pdf","markdown":"https://ijcai.org/proceedings/2024/265.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/265","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":265,"pages":{"first":2397,"last":2405},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Self-supervised Learning"],"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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