AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Negative Influence Minimizing by Blocking Nodes in Social Networks
Senzhang Wang, Xiaojian Zhao, Yan Chen, Zhoujun Li, Kai Zhang, Jiali Xia

Last modified: 2013-06-29


Social networks are becoming vital platforms for the spread of positive information such as innovations and negative information propagation like malicious rumors. In this paper, we address the problem of minimizing the influence of negative information. When negative information such as a rumor emerges in the socialnetwork and part of users have already adopted it, our goal is to minimize the size of ultimately contaminatedusers by discovering and blocking k uninfected users. A greedy method for efficiently finding a good approximate solution to this problem is proposed. The comparison experimental results on the Enron email network dataset demonstrate our proposed method is more effective than centrality based methods, such as degreecentrality, betweenness centrality and PageRank.

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