{"type":"ScholarlyArticle","title":"CONGREGATE: Contrastive Graph Clustering in Curvature Spaces","authors":[{"name":"Li Sun","slug":"li-sun","given":"Li","family":"Sun","affiliation":"North China Electric Power University , Beijing 102206 , China"},{"name":"Feiyang Wang","slug":"feiyang-wang","given":"Feiyang","family":"Wang","affiliation":"Beijing University of Posts and Telecommunications , Beijing 100876 , China"},{"name":"Junda Ye","slug":"junda-ye","given":"Junda","family":"Ye","affiliation":"Beijing University of Posts and Telecommunications , Beijing 100876 , China"},{"name":"Hao Peng","slug":"hao-peng","given":"Hao","family":"Peng","affiliation":"Beihang University , Beijing 100191 , China"},{"name":"Philip S. Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"Department of Computer Science , University of Illinois at Chicago , IL , USA"}],"abstract":"Graph clustering is a longstanding research topic, and has achieved remarkable success with the deep learning methods in recent years. Nevertheless, we observe that several important issues largely remain open. On the one hand, graph clustering from the geometric perspective is appealing but has rarely been touched before, as it lacks a promising space for geometric clustering. On the other hand, contrastive learning boosts the deep graph clustering but usually struggles in either graph augmentation or hard sample mining. To bridge this gap, we rethink the problem of graph clustering from geometric perspective and, to the best of our knowledge, make the first attempt to introduce a heterogeneous curvature space to graph clustering problem. Correspondingly, we present a novel end-to-end contrastive graph clustering model named CONGREGATE, addressing geometric graph clustering with Ricci curvatures. To support geometric clustering, we construct a theoretically grounded Heterogeneous Curvature Space where deep representations are generated via the product of the proposed fully Riemannian graph convolutional nets. Thereafter, we train the graph clusters by an augmentation-free reweighted contrastive approach where we pay more attention to both hard negatives and hard positives in our curvature space. Empirical results on real-world graphs show that our model outperforms the state-of-the-art competitors.","doi":"10.24963/ijcai.2023/255","url":"https://ijcai.org/proceedings/2023/255","pdf":"https://ijcai.org/proceedings/2023/0255.pdf","markdown":"https://ijcai.org/proceedings/2023/255.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/255","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":255,"pages":{"first":2296,"last":2305},"keywords":["Machine Learning: ML: Clustering","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"Gregor Bachmann, Gary Bécigneul, and Octavian Ganea. 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