{"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/ensemble-clustering-for-graphs-comparisons","title":"Ensemble Clustering for Graphs: Comparisons and Applications","arxiv_id":"1903.08012","date":"2019-03-19","proceeding":null,"authors":["Valérie Poulin","François Théberge"],"abstract":"We recently proposed a new ensemble clustering algorithm for graphs (ECG)\nbased on the concept of consensus clustering. We validated our approach by\nreplicating a study comparing graph clustering algorithms over benchmark\ngraphs, showing that ECG outperforms the leading algorithms. In this paper, we\nextend our comparison by considering a wider range of parameters for the\nbenchmark, generating graphs with different properties. We provide new\nexperimental results showing that the ECG algorithm alleviates the well-known\nresolution limit issue, and that it leads to better stability of the\npartitions. We also illustrate how the ensemble obtained with ECG can be used\nto quantify the presence of community structure in the graph, and to zoom in on\nthe sub-graph most closely associated with seed vertices. Finally, we\nillustrate further applications of ECG by comparing it to previous results for\ncommunity detection on weighted graphs, and community-aware anomaly detection.","url_abs":"http://arxiv.org/abs/1903.08012v1","url_pdf":"http://arxiv.org/pdf/1903.08012v1.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":"ensemble-clustering-for-graphs-comparisons","repo_url":"https://github.com/ftheberge/graph-partition-and-measures","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"ensemble-clustering-for-graphs-comparisons","repo_url":"https://github.com/ftheberge/Ensemble-Clustering-for-Graphs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"community-detection","task_name":"Community Detection"},{"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}