Affiliation:
1. Arizona State University
2. Virginia Tech
3. Rutgers University
4. University of New Mexico, New Mexico, USA
5. Carnegie Mellon University, Pittsburgh, PA
Abstract
Large graphs are prevalent in many applications and enable a variety of information dissemination processes, e.g., meme, virus, and influence propagation. How can we optimize the underlying graph structure to affect the outcome of such dissemination processes in a desired way (e.g., stop a virus propagation, facilitate the propagation of a piece of good idea, etc)? Existing research suggests that the leading eigenvalue of the underlying graph is the key metric in determining the so-called epidemic threshold for a variety of dissemination models. In this paper, we study the problem of how to optimally place a set of edges (e.g., edge deletion and edge addition) to optimize the leading eigenvalue of the underlying graph, so that we can guide the dissemination process in a desired way. We propose effective, scalable algorithms for edge deletion and edge addition, respectively. In addition, we reveal the intrinsic relationship between edge deletion and node deletion problems. Experimental results validate the effectiveness and efficiency of the proposed algorithms.
Funder
Defense Advanced Research Projects Agency
Lawrence Livermore National Laboratory
National Endowment for the Humanities
Oak Ridge National Laboratory
National Science Foundation
Defense Threat Reduction Agency
National Institute for Health Research
Region II University Transportation Center
U.S. Army Research Laboratory
Maryland Procurement Office
Publisher
Association for Computing Machinery (ACM)
Reference77 articles.
1. Estimating rates of rare events at multiple resolutions
2. Lars Backstrom and Jure Leskovec. 2011. Supervised random walks: Predicting and recommending links in social networks. In WSDM. Hong Kong China 635--644. 10.1145/1935826.1935914 Lars Backstrom and Jure Leskovec. 2011. Supervised random walks: Predicting and recommending links in social networks. In WSDM. Hong Kong China 635--644. 10.1145/1935826.1935914
3. Working for Influence: Effect of Network Density and Modularity on Diffusion in Networks
4. Interacting viruses in networks
5. A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades
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