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M. Kimura, Kazumi Saito, R. Nakano (2007)
Extracting Influential Nodes for Information Diffusion on a Social Network
Pedro Domingos, Matthew Richardson (2001)
Mining the network value of customers
(2003)
Blocking Links to Minimize Contamination Spread in a Social Network
D. Callaway, M. Newman, M. Newman, S. Strogatz, D. Watts, D. Watts (2000)
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ACM Journal Name
D. Gruhl, R. Guha, D. Liben-Nowell, A. Tomkins (2004)
Information diffusion through blogspace
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J. Leskovec, Andreas Krause, Carlos Guestrin, C. Faloutsos, J. Vanbriesen, N. Glance (2007)
Cost-effective outbreak detection in networks
Mark Newman, Mark Newman, Michelle Girvan, Michelle Girvan (2003)
Finding and evaluating community structure in networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 69 2 Pt 2
M. Newman (2003)
The Structure and Function of Complex NetworksSIAM Rev., 45
D. Kempe, J. Kleinberg, É. Tardos (2003)
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M. Newman, M. Newman, Juyong Park, Juyong Park (2003)
Why social networks are different from other types of networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 68 3 Pt 2
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Received September ACM Transactions on Knowledge Discovery from Data
Matthew Richardson, Pedro Domingos (2002)
Mining knowledge-sharing sites for viral marketingProceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
D. Gruhl, D. Liben-Nowell, R. Guha, A. Tomkins (2004)
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Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Minimizing the Spread of Contamination by Blocking Links in a Network
R. Albert, Hawoong Jeong, A. Barabási (2000)
Error and attack tolerance of complex networksNature, 406
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.
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Apr 1, 2009
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