{"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/lookout-on-time-evolving-graphs-succinctly","title":"LookOut on Time-Evolving Graphs: Succinctly Explaining Anomalies from Any Detector","arxiv_id":"1710.05333","date":"2017-10-15","proceeding":null,"authors":["Nikhil Gupta","Dhivya Eswaran","Neil Shah","Leman Akoglu","Christos Faloutsos"],"abstract":"Why is a given node in a time-evolving graph ($t$-graph) marked as an anomaly by an off-the-shelf detection algorithm? Is it because of the number of its outgoing or incoming edges, or their timings? How can we best convince a human analyst that the node is anomalous? Our work aims to provide succinct, interpretable, and simple explanations of anomalous behavior in $t$-graphs (communications, IP-IP interactions, etc.) while respecting the limited attention of human analysts. Specifically, we extract key features from such graphs, and propose to output a few pair (scatter) plots from this feature space which \"best\" explain known anomalies. To this end, our work has four main contributions: (a) problem formulation: we introduce an \"analyst-friendly\" problem formulation for explaining anomalies via pair plots, (b) explanation algorithm: we propose a plot-selection objective and the LookOut algorithm to approximate it with optimality guarantees, (c) generality: our explanation algorithm is both domain- and detector-agnostic, and (d) scalability: we show that LookOut scales linearly on the number of edges of the input graph. Our experiments show that LookOut performs near-ideally in terms of maximizing explanation objective on several real datasets including Enron e-mail and DBLP coauthorship. Furthermore, LookOut produces fast, visually interpretable and intuitive results in explaining \"ground-truth\" anomalies from Enron, DBLP and LBNL (computer network) data.","url_abs":"http://arxiv.org/abs/1710.05333v1","url_pdf":"http://arxiv.org/pdf/1710.05333v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"lookout-on-time-evolving-graphs-succinctly","repo_url":"https://github.com/NikhilGupta1997/Lookout","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"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}