{"type":"ScholarlyArticle","title":"Dual Contrastive Graph-Level Clustering with Multiple Cluster Perspectives Alignment","authors":[{"name":"Jinyu Cai","slug":"jinyu-cai","given":"Jinyu","family":"Cai","affiliation":""},{"name":"Yunhe Zhang","slug":"yunhe-zhang","given":"Yunhe","family":"Zhang","affiliation":""},{"name":"Jicong Fan","slug":"jicong-fan","given":"Jicong","family":"Fan","affiliation":""},{"name":"Yali Du","slug":"yali-du","given":"Yali","family":"Du","affiliation":""},{"name":"Wenzhong Guo","slug":"wenzhong-guo","given":"Wenzhong","family":"Guo","affiliation":""}],"abstract":"Graph-level clustering, which is essential in medical, biomedical, and social network data analysis, aims to group a set of graphs into various clusters.   However, existing methods generally rely on a single clustering criterion, e.g., $k$-means, which limits their abilities to fully exploit the complex Euclidean and structural information inherent in graphs. To bridge this gap, we propose a dual contrastive graph-level clustering (DCGLC) method in this paper. DCGLC leverages graph contrastive learning and introduces the Euclidian-based and subspace-based cluster heads to capture the cluster information from different cluster perspectives. To overcome the inconsistency estimations and fuse the cluster information of multiple cluster heads, we propose a contrastive mechanism to align the cluster information derived from them. The cluster-perspective contrast facilitates the capture of more comprehensive cluster information. Importantly, DCGLC is an end-to-end framework in which graph contrastive learning and cluster-perspective contrast are mutually improved. We demonstrate the superiority of DCGLC over the state-of-the-art baselines on numerous graph benchmarks.","doi":"10.24963/ijcai.2024/417","url":"https://ijcai.org/proceedings/2024/417","pdf":"https://ijcai.org/proceedings/2024/0417.pdf","markdown":"https://ijcai.org/proceedings/2024/417.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/417","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":"Machine Learning","track":"Main Track","number":417,"pages":{"first":3770,"last":3779},"keywords":["Machine Learning: ML: Clustering","Machine Learning: ML: Unsupervised learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}