{"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/concept-drift-and-anomaly-detection-in-graph","title":"Concept Drift and Anomaly Detection in Graph Streams","arxiv_id":"1706.06941","date":"2017-06-21","proceeding":null,"authors":["Daniele Zambon","Cesare Alippi","Lorenzo Livi"],"abstract":"Graph representations offer powerful and intuitive ways to describe data in a\nmultitude of application domains. Here, we consider stochastic processes\ngenerating graphs and propose a methodology for detecting changes in\nstationarity of such processes. The methodology is general and considers a\nprocess generating attributed graphs with a variable number of vertices/edges,\nwithout the need to assume one-to-one correspondence between vertices at\ndifferent time steps. The methodology acts by embedding every graph of the\nstream into a vector domain, where a conventional multivariate change detection\nprocedure can be easily applied. We ground the soundness of our proposal by\nproving several theoretical results. In addition, we provide a specific\nimplementation of the methodology and evaluate its effectiveness on several\ndetection problems involving attributed graphs representing biological\nmolecules and drawings. Experimental results are contrasted with respect to\nsuitable baseline methods, demonstrating the effectiveness of our approach.","url_abs":"http://arxiv.org/abs/1706.06941v3","url_pdf":"http://arxiv.org/pdf/1706.06941v3.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":"concept-drift-and-anomaly-detection-in-graph","repo_url":"https://github.com/dzambon/cdg","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"change-detection","task_name":"Change Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}