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28 July 2023 An acceleration method for influence blocking maximization via martingale
Zhijin Wu, Wenguo Yang, Suixiang Gao
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Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127562S (2023) https://doi.org/10.1117/12.2685914
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China
Abstract
The spread of rumors in social networks can do great harm to the society, so it is significant to limit the propagation of misinformation. One solution to block rumor is to broadcast the anti-rumor information. How to select users of social networks to spread information against rumor can be abstracted as the problem of influence blocking maximization (IBM). The problem can be described as choosing k nodes from a social graph to minimize the number of nodes that adopt rumor at the end of the spread process. The state-of-the-art IBM algorithm IBMM has a ( 1 - 1/e - ε)-approximation guarantee. However, we noticed that the algorithm could cause unnecessary cost of calculation. Therefore, we modify IBMM and propose the IBM-N algorithm. And the results of a series of experiments conducted on both synthetic and real world data sets demonstrate the performance efficiency of our algorithm.
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Zhijin Wu, Wenguo Yang, and Suixiang Gao "An acceleration method for influence blocking maximization via martingale", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127562S (28 July 2023); https://doi.org/10.1117/12.2685914
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KEYWORDS
Social networks
Mathematical modeling
Simulations
Design and modelling
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