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Research on Critical Nodes Algorithm in Social Complex Networks

Research on Critical Nodes Algorithm in Social Complex Networks 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Physics de Gruyter

Research on Critical Nodes Algorithm in Social Complex Networks

Open Physics , Volume 15 (1): 6 – Mar 16, 2017

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Publisher
de Gruyter
Copyright
© 2017 X.-G. Wang
ISSN
2391-5471
eISSN
2391-5471
DOI
10.1515/phys-2017-0008
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Open Physicsde Gruyter

Published: Mar 16, 2017

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