Cost-effective Outbreak Detection in Networks

Given a water distribution network, where should one place sensors to quickly detect contaminants? Or, which blogs should be 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. The authors present a general methodology for near optimal sensor placement in these and related problems. They demonstrate that many realistic outbreak detection objectives (e.g., detection likelihood, population affected) exhibit the property of "Submodularity". They 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.