{"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/non-linear-attributed-graph-clustering-by","title":"Non-linear Attributed Graph Clustering by Symmetric NMF with PU Learning","arxiv_id":"1810.00946","date":"2018-09-21","proceeding":null,"authors":["Seiji Maekawa","Koh Takeuch","Makoto Onizuka"],"abstract":"We consider the clustering problem of attributed graphs. Our challenge is how\nwe can design an effective and efficient clustering method that precisely\ncaptures the hidden relationship between the topology and the attributes in\nreal-world graphs. We propose Non-linear Attributed Graph Clustering by\nSymmetric Non-negative Matrix Factorization with Positive Unlabeled Learning.\nThe features of our method are three holds. 1) it learns a non-linear\nprojection function between the different cluster assignments of the topology\nand the attributes of graphs so as to capture the complicated relationship\nbetween the topology and the attributes in real-world graphs, 2) it leverages\nthe positive unlabeled learning to take the effect of partially observed\npositive edges into the cluster assignment, and 3) it achieves efficient\ncomputational complexity, $O((n^2+mn)kt)$, where $n$ is the vertex size, $m$ is\nthe attribute size, $k$ is the number of clusters, and $t$ is the number of\niterations for learning the cluster assignment. We conducted experiments\nextensively for various clustering methods with various real datasets to\nvalidate that our method outperforms the former clustering methods regarding\nthe clustering quality.","url_abs":"http://arxiv.org/abs/1810.00946v1","url_pdf":"http://arxiv.org/pdf/1810.00946v1.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":"non-linear-attributed-graph-clustering-by","repo_url":"https://github.com/seijimaekawa/NAGC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"graph-clustering","task_name":"Graph Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}