Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding effectors in social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding influential seed successors in social networks
Proceedings of the 21st international conference companion on World Wide Web
Dynamic selection of activation targets to boost the influence spread in social networks
Proceedings of the 21st international conference companion on World Wide Web
Spread of information in a social network using influential nodes
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Exploring celebrity dynamics on Twitter
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
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Given a social network, who are the key players controlling the bottlenecks of influence propagation if some persons would like to activate specific individuals? In this paper, we tackle the problem of selecting a set of k mediator nodes as the influential gateways whose existence determines the activation probabilities of targeted nodes from some given seed nodes. We formally define the k-Mediators problem. To have an effective and efficient solution, we propose a three-step greedy method by considering the probabilistic influence and the structural connectivity on the pathways from sources to targets. To the best of our knowledge, this is the first work to consider the k-Mediators problem in networks. Experiments on the DBLP co-authorship graph show the effectiveness and efficiency of the proposed method.