Generalized opinion dynamics model for social trust networks

Changxiang He (), Jiayuan Zeng, Guang Zhang and Shuting Liu
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Changxiang He: University of Shanghai for Science and Technology
Jiayuan Zeng: University of Shanghai for Science and Technology
Guang Zhang: University of Shanghai for Science and Technology
Shuting Liu: University of Shanghai for Science and Technology

Journal of Combinatorial Optimization, 2022, vol. 44, issue 5, No 22, 3662 pages

Abstract: Abstract The study of opinion dynamics model on social networks is one of the hot spots in the field of social sciences. In this paper, we propose a generalized opinion dynamics model, which dynamically compute each person’s expressed opinion, to solve the opinion maximization problem for social trust networks. In the model, we propose a new, reasonable and interpretable confidence index $$\alpha _i$$ α i , which is different from randomly selected $$\alpha _i$$ α i and is determined by both person’s social status and the evaluation of his/her predecessors. By using the theory of diagonally dominant, we obtain the optimal analytic solution of the Nash equilibrium with maximum overall opinion. In addition, we design an efficient traditional ADMM algorithm with $$l_1$$ l 1 -regulations to maximize the overall opinion. A series of experiments are conducted, and the experimental results show that the proposed model is superior to the state-of-the-art in four datasets. The average benefit has promoted $$67.5\%$$ 67.5 % , $$83.2\%$$ 83.2 % , $$31.5\%$$ 31.5 % , and $$33.7\%$$ 33.7 % in solving the internal opinion problem and $$215.2\%$$ 215.2 % , $$225.1\%$$ 225.1 % , $$33.0\%$$ 33.0 % , $$21.2\%$$ 21.2 % in solving the expressed opinion problems on four datasets, respectively.

Keywords: Opinion maximization; Social trust networks; Confidence index; Optimization; Limited budget (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10878-022-00913-7

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