Machine Learning Department
School of Computer Science, Carnegie Mellon University


Cost-effective Outbreak Detection in Networks

Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen*, Natalie Glance**

June 2007

This works appears in the
Proceeedings of the 13th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining


Keywords: Graphs, information cascades, virus propagation, sensor placement, submodular functions

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 (sensor locations, blogs) have different costs.

We evaluate our approach on several large real-world problems, including a model of a water distribution network from the 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 scales, achieving speedups and savings in storage of several orders of magnitude. We also show how the approach leads to deeper insights in both applications, answering multicriteria trade-off, cost-sensitivity and generalization questions.

31 pages

*Department of Civil and Environmental Engineering, Carnegie Mellon University
**Nielsen Buzzmetrics, Pittsburgh, PA

SCS Technical Report Collection
School of Computer Science homepage

This page maintained by