Cost-effective outbreak detection in networks

Given a water distribution network, where should we place sensors to quickly detect contaminants? Or, which blogs should we read to avoid missing important stories? These seemingly different problems share common structure: Outbreak detection can be modeled as selecting nodes (sensor locations, blogs) in a network, in order to detect the spreading of a virus or information as quickly as possible. We present a general methodology for near optimal sensor placement in these and related problems. We demonstrate that 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, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm. We also derive online bounds on the quality of the placements obtained by any algorithm. Our algorithms and bounds also handle cases where nodes (sen...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne M. VanBriesen, Jure Leskovec, Natalie S. Glance
• Keywords: Data Mining, KDD 2007, Optimal Placements, Optimal Sensor Placement, Sensor Placements