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AbstractDiscovering critical nodes in social networks has many important applications and has attracted more and more institutions and scholars. How to determine the K critical nodes with the most influence in a social network is a NP (define) problem. Considering the widespread community structure, this paper presents an algorithm for discovering critical nodes based on two information diffusion models and obtains each node’s marginal contribution by using a Monte-Carlo method in social networks. The solution of the critical nodes problem is the K nodes with the highest marginal contributions. The feasibility and effectiveness of our method have been verified on two synthetic datasets and four real datasets.
Open Physics – de Gruyter
Published: Mar 16, 2017
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