{"type":"ScholarlyArticle","title":"CSGCL: Community-Strength-Enhanced Graph Contrastive Learning","authors":[{"name":"Han Chen","slug":"han-chen","given":"Han","family":"Chen","affiliation":"School of Computer Science and Technology , Huazhong University of Science and Technology; Institute of Artificial Intelligence , Huazhong University of Science and Technology"},{"name":"Ziwen Zhao","slug":"ziwen-zhao","given":"Ziwen","family":"Zhao","affiliation":"School of Computer Science and Technology , Huazhong University of Science and Technology"},{"name":"Yuhua Li","slug":"yuhua-li","given":"Yuhua","family":"Li","affiliation":"School of Computer Science and Technology , Huazhong University of Science and Technology"},{"name":"Yixiong Zou","slug":"yixiong-zou","given":"Yixiong","family":"Zou","affiliation":"School of Computer Science and Technology , Huazhong University of Science and Technology"},{"name":"Ruixuan Li","slug":"ruixuan-li","given":"Ruixuan","family":"Li","affiliation":"School of Computer Science and Technology , Huazhong University of Science and Technology"},{"name":"Rui Zhang","slug":"rui-zhang","given":"Rui","family":"Zhang","affiliation":"Tsinghua University; † Tsinghua University"}],"abstract":"Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years. However, the underlying community semantics has not been well explored by most previous GCL methods. Research that attempts to leverage communities in GCL regards them as having the same influence on the graph, leading to extra representation errors. To tackle this issue, we define ''community strength'' to measure the difference of influence among communities. Under this premise, we propose a Community-Strength-enhanced Graph Contrastive Learning (CSGCL) framework to preserve community strength throughout the learning process. Firstly, we present two novel graph augmentation methods, Communal Attribute Voting (CAV) and Communal Edge Dropping (CED), where the perturbations of node attributes and edges are guided by community strength. Secondly, we propose a dynamic ''Team-up'' contrastive learning scheme, where community strength is used to progressively fine-tune the contrastive objective. We report extensive experiment results on three downstream tasks: node classification, node clustering, and link prediction. CSGCL achieves state-of-the-art performance compared with other GCL methods, validating that community strength brings effectiveness and generality to graph representations. Our code is available at https://github.com/HanChen-HUST/CSGCL.","doi":"10.24963/ijcai.2023/229","url":"https://ijcai.org/proceedings/2023/229","pdf":"https://ijcai.org/proceedings/2023/0229.pdf","markdown":"https://ijcai.org/proceedings/2023/229.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/229","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":"Data Mining","track":"Main Track","number":229,"pages":{"first":2059,"last":2067},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Self-supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Ana and Jain, 2003] LN Fred Ana and Anil K Jain. 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