Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model

Authors:
Masahiro Kimura;Kazumi Saito;Hiroshi Motoda
Affiliations:
Department of Electronics and Informatics, Ryukoku University, Otsu, Japan 520-2194;School of Administration and Informatics, University of Shizuoka, Shizuoka, Japan 422-8526;Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan 567-0047
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

Citing 7
Cited 1

Graph structure in the Web

Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Mining the network value of customers

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace

Proceedings of the 13th international conference on World Wide Web
Extracting influential nodes for information diffusion on a social network

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Minimizing the spread of contamination by blocking links in a network

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2

Controlling infection by blocking nodes and links simultaneously

WINE'11 Proceedings of the 7th international conference on Internet and Network Economics

Quantified Score

Hi-index	0.00

Visualization

Abstract

We address the problem of minimizing the spread of undesirable things, such as computer viruses and malicious rumors, by blocking a limited number of links in a network. This optimization problem called the contamination minimization problem is, not only yet another approach to the problem of preventing the spread of contamination by removing nodes in a network, but also a problem that is converse to the influence maximization problem of finding the most influential nodes in a social network for information diffusion. We adapted the method which we developed for the independent cascade model, known for a model for the spread of epidemic disease, to the contamination minimization problem under the linear threshold model, a model known for the propagation of innovation which is considerably different in nature. Using large real networks, we demonstrate experimentally that the proposed method significantly outperforms conventional link-removal methods.