{"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/anomaly-detection-in-the-dynamics-of-web-and","title":"Anomaly detection in the dynamics of web and social networks","arxiv_id":"1901.09688","date":"2019-01-22","proceeding":null,"authors":["Volodymyr Miz","Benjamin Ricaud","Kirell Benzi","Pierre Vandergheynst"],"abstract":"In this work, we propose a new, fast and scalable method for anomaly\ndetection in large time-evolving graphs. It may be a static graph with dynamic\nnode attributes (e.g. time-series), or a graph evolving in time, such as a\ntemporal network. We define an anomaly as a localized increase in temporal\nactivity in a cluster of nodes. The algorithm is unsupervised. It is able to\ndetect and track anomalous activity in a dynamic network despite the noise from\nmultiple interfering sources. We use the Hopfield network model of memory to\ncombine the graph and time information. We show that anomalies can be spotted\nwith a good precision using a memory network. The presented approach is\nscalable and we provide a distributed implementation of the algorithm. To\ndemonstrate its efficiency, we apply it to two datasets: Enron Email dataset\nand Wikipedia page views. We show that the anomalous spikes are triggered by\nthe real-world events that impact the network dynamics. Besides, the structure\nof the clusters and the analysis of the time evolution associated with the\ndetected events reveals interesting facts on how humans interact, exchange and\nsearch for information, opening the door to new quantitative studies on\ncollective and social behavior on large and dynamic datasets.","url_abs":"http://arxiv.org/abs/1901.09688v1","url_pdf":"http://arxiv.org/pdf/1901.09688v1.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":"anomaly-detection-in-the-dynamics-of-web-and","repo_url":"https://github.com/mizvol/WikiBrain","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"anomaly-detection-in-the-dynamics-of-web-and","repo_url":"https://github.com/epfl-lts2/sparkwiki","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":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}