{"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/cost-effective-outbreak-detection-in-networks","title":"Cost-effective Outbreak Detection in Networks","arxiv_id":null,"date":"2007-08-12","proceeding":"SIGKDD 2007 8","authors":["Jure Leskovec","Andreas Krause","Carlos Guestrin","Christos Faloutsos","Jeanne VanBriesen","Natalie Glance"],"abstract":"Given a water distribution network, where should we place\r\nsensors to quickly detect contaminants? Or, which blogs\r\nshould we read to avoid missing important stories?\r\nThese seemingly different problems share common structure: Outbreak detection can be modeled as selecting nodes\r\n(sensor locations, blogs) in a network, in order to detect the\r\nspreading of a virus or information as quickly as possible.\r\nWe present a general methodology for near optimal sensor\r\nplacement in these and related problems. We demonstrate\r\nthat many realistic outbreak detection objectives (e.g., detection likelihood, population affected) exhibit the property of “submodularity”. We exploit submodularity to develop an efficient algorithm that scales to large problems,\r\nachieving near optimal placements, while being 700 times\r\nfaster than a simple greedy algorithm. We also derive online bounds on the quality of the placements obtained by\r\nany algorithm. Our algorithms and bounds also handle cases\r\nwhere nodes (sensor locations, blogs) have different costs.\r\nWe evaluate our approach on several large real-world problems, including a model of a water distribution network from\r\nthe EPA, and real blog data. The obtained sensor placements are provably near optimal, providing a constant fraction of the optimal solution. We show that the approach\r\nscales, achieving speedups and savings in storage of several\r\norders of magnitude. We also show how the approach leads\r\nto deeper insights in both applications, answering multicriteria trade-off, cost-sensitivity and generalization questions","url_abs":"https://dl.acm.org/doi/10.1145/1281192.1281239","url_pdf":"https://www.cs.cmu.edu/~jure/pubs/detect-kdd07.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":"cost-effective-outbreak-detection-in-networks","repo_url":"https://github.com/zahraDehghanian97/CELF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}