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Blocking links to minimize contamination spread in a social network

Blocking links to minimize contamination spread in a social network We address the problem of minimizing the propagation of undesirable things, such as computer viruses or malicious rumors, by blocking a limited number of links in a network, which is converse to the influence maximization problem in which the most influential nodes for information diffusion is searched in a social network. This minimization problem is more fundamental than the problem of preventing the spread of contamination by removing nodes in a network. We introduce two definitions for the contamination degree of a network, accordingly define two contamination minimization problems, and propose methods for efficiently finding good approximate solutions to these problems on the basis of a naturally greedy strategy. Using large social networks, we experimentally demonstrate that the proposed methods outperform conventional link-removal methods. We also show that unlike the case of blocking a limited number of nodes, the strategy of removing nodes with high out-degrees is not necessarily effective for these problems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Blocking links to minimize contamination spread in a social network

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References (18)

Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Sociology
ISSN
1556-4681
DOI
10.1145/1514888.1514892
Publisher site
See Article on Publisher Site

Abstract

We address the problem of minimizing the propagation of undesirable things, such as computer viruses or malicious rumors, by blocking a limited number of links in a network, which is converse to the influence maximization problem in which the most influential nodes for information diffusion is searched in a social network. This minimization problem is more fundamental than the problem of preventing the spread of contamination by removing nodes in a network. We introduce two definitions for the contamination degree of a network, accordingly define two contamination minimization problems, and propose methods for efficiently finding good approximate solutions to these problems on the basis of a naturally greedy strategy. Using large social networks, we experimentally demonstrate that the proposed methods outperform conventional link-removal methods. We also show that unlike the case of blocking a limited number of nodes, the strategy of removing nodes with high out-degrees is not necessarily effective for these problems.

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Apr 1, 2009

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