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
Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Blocking links to minimize contamination spread in a social network
ACM Transactions on Knowledge Discovery from Data (TKDD)
What Does an Information Diffusion Model Tell about Social Network Structure?
Knowledge Acquisition: Approaches, Algorithms and Applications
OASNET: an optimal allocation approach to influence maximization in modular social networks
Proceedings of the 2010 ACM Symposium on Applied Computing
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Discovery of super-mediators of information diffusion in social networks
DS'10 Proceedings of the 13th international conference on Discovery science
Detecting changes in opinion value distribution for voter model
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Maximizing influence spread in modular social networks by optimal resource allocation
Expert Systems with Applications: An International Journal
The complexity and approximability of minimum contamination problems
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
Learning diffusion probability based on node attributes in social networks
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Behavioral analyses of information diffusion models by observed data of social network
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Graph embedding on spheres and its application to visualization of information diffusion data
Proceedings of the 21st international conference companion on World Wide Web
Controlling infection by blocking nodes and links simultaneously
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Detecting changes in information diffusion patterns over social networks
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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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, a dual problem to the influence maximization problem of finding the most influential nodes in a social network for information diffusion. This minimization problem is another approach to the problem of preventing the spread of contamination by removing nodes in a network. We propose a method for efficiently finding a good approximate solution to this problem based on a naturally greedy strategy. Using large real networks, we demonstrate experimentally that the proposed method significantly outperforms conventional link-removal methods. We also show that unlike the strategy of removing nodes, blocking links between nodes with high out-degrees is not necessarily effective.