{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/attributed-graph-clustering-with-dual","title":"Attributed Graph Clustering with Dual Redundancy Reduction","arxiv_id":null,"date":"2022-04-18","proceeding":"Conference 2022 4","authors":["Lei Gong","Sihang Zhou","Wenxuan Tu and Xinwang Liu∗"],"abstract":"Attributed graph clustering is a basic yet essential\r\nmethod for graph data exploration. Recent efforts\r\nover graph contrastive learning have achieved impressive clustering performance. However, we observe that the commonly adopted InfoMax operation tends to capture redundant information, limiting the downstream clustering performance. To\r\nthis end, we develop a novel method termed attributed graph clustering with dual redundancy reduction (AGC-DRR) to reduce the information redundancy in both input space and latent feature\r\nspace. Specifcally, for the input space redundancy reduction, we introduce an adversarial learning mechanism to adaptively learn a redundant\r\nedge-dropping matrix to ensure the diversity of the\r\ncompared sample pairs. To reduce the redundancy\r\nin the latent space, we force the correlation matrix of the cross-augmentation sample embedding\r\nto approximate an identity matrix. Consequently,\r\nthe learned network is forced to be robust against\r\nperturbation while discriminative against different samples. Extensive experiments have demonstrated that AGC-DRR outperforms the state-ofthe-art clustering methods on most of our benchmarks. The corresponding code is available at\r\nhttps://github.com/gongleii/AGC-DRR.","url_abs":"https://www.ijcai.org/proceedings/2022/0418.pdf","url_pdf":"https://www.ijcai.org/proceedings/2022/0418.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"attributed-graph-clustering-with-dual","repo_url":"https://github.com/gongleii/AGC-DRR","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"attributed-graph-clustering-with-dual","repo_url":"https://github.com/chengglmotto/AGC-DRR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"graph-clustering","task_name":"Graph Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}