Mining the network value of customers
Proceedings of the seventh 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
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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Influence Maximization aims to find the top-K influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Most of the existing studies focus on greedy algorithms and mainly suffer from low computational efficiency, limiting its application to real-world social networks. In this paper, we propose a novel approach ESMCE that can significantly reduce the running time. Utilizing a power-law exponent supervised Monte Carlo method, ESMCE is able to efficiently estimate the influence spread for nodes with specified precision by randomly sampling only a small portion of child nodes, thus is well suitable for large-scale social networks. Extensive experiments on five real-world social network demonstrate that, compared with state-of-the-art influence maximization algorithms, ESMCE is able to achieve more than an order of magnitude speedup in execution time with only negligible error (2.21% on average) in influence spread.