Finding Near-Optimal Groups of Epidemic Spreaders in a Complex Network
Abstract
In this paper, we present algorithms to find near-optimal sets of epidemic spreaders in complex networks. We extend the notion of local-centrality, a centrality measure previously shown to correspond with a nodes ability to spread an epidemic, to sets of nodes by introducing combinatorial local centrality. Though we prove that finding a set of nodes that maximizes this new measure is NP-hard, good approximations are available. We show that a strictly greedy approach obtains the best approximation ratio unless P = NP and then formulate a modified version of this approach that leverages qualities of the network to achieve a faster runtime while maintaining this theoretical guarantee. We perform an experimental evaluation on samples from several different network structures which demonstrate that our algorithm maximizes combinatorial local centrality and consistently chooses the most effective set of nodes to spread infection under the SIR model, relative to selecting the top nodes using many common centrality measures. We also demonstrate that the optimized algorithm we develop scales effectively.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 02, 2014
- Accession Number
- AD1057616
Entities
People
- Brian Macdonald
- Geoffrey Moores
- Nicholas Howard
- Paulo Shakarian
Organizations
- United States Military Academy