Influence sets based on reverse nearest neighbor queries
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UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In this paper, we introduce a new type of influence maximization problem, i.e., choosing a smallest subset of members from implicit social networks for maximizing the spread of influence. These kinds of networks differ from some well studied explicit social networks in that the nodes are not directly connected. We propose a reverse k-nearest neighbor approach to measure a user's social network potential (SNP). Further we sequentially select seed nodes with the largest SNP value from the user set. Experimental results on real web data demonstrate the effectiveness of our proposed solution.