{"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/sedanspot-detecting-anomalies-in-edge-streams","title":"SEDANSPOT: Detecting Anomalies in Edge Streams","arxiv_id":null,"date":"2018-11-20","proceeding":"ICDM 2018 11","authors":["Dhivya Eswaran","Christos Faloutsos"],"abstract":"Given a stream of edges from a time-evolving\r\n(un)weighted (un)directed graph, we consider the problem of detecting anomalous edges in near real-time using sublinear memory.\r\nWe propose SEDANSPOT, a principled randomized algorithm,\r\nwhich exploits two tell-tale signs of anomalous edges: they tend\r\nto (i) occur as bursts of activity and (ii) connect parts of the graph\r\nwhich are sparsely connected. SEDANSPOT has the following desirable properties: (a) Burst resistance: It provably downsamples\r\nedges from bursty periods of network traffic, (b) Holistic scoring:\r\nIt takes into account the whole (sampled) graph while scoring\r\nthe anomalousness of an edge, giving diminishing importance to\r\nfar-away neighbors, (c) Efficiency: It supports fast updates and\r\nscoring and hence can be efficiently maintained over stream;\r\nfurther, it can detect anomalous edges in sublinear space and\r\nconstant time per edge. Through experiments on real-world data,\r\nwe demonstrate that SEDANSPOT is 3× faster and 270% more\r\naccurate (in terms of AUC) than the state-of-the-art.","url_abs":"https://www.cs.cmu.edu/~deswaran/papers/icdm18-sedanspot.pdf","url_pdf":"https://www.cs.cmu.edu/~deswaran/papers/icdm18-sedanspot.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":"sedanspot-detecting-anomalies-in-edge-streams","repo_url":"https://github.com/dhivyaeswaran/sedanspot","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection-in-edge-streams","task_name":"Anomaly Detection in Edge Streams"}],"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}