Opinion Maximization in Social Networks by Modifying Internal Opinions
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918382571356160 |
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| author | Wang, Gengyu Zhang, Runze Zhang, Zhongzhi |
| author_facet | Wang, Gengyu Zhang, Runze Zhang, Zhongzhi |
| contents | Public opinion governance in social networks is critical for public health campaigns, political elections, and commercial marketing. In this paper, we addresse the problem of maximizing overall opinion in social networks by strategically modifying the internal opinions of key nodes. Traditional matrix inversion methods suffer from prohibitively high computational costs, prompting us to propose two efficient sampling-based algorithms. Furthermore, we develop a deterministic asynchronous algorithm that exactly identifies the optimal set of nodes through asynchronous update operations and progressive refinement, ensuring both efficiency and precision. Extensive experiments on real-world datasets demonstrate that our methods outperform baseline approaches. Notably, our asynchronous algorithm delivers exceptional efficiency and accuracy across all scenarios, even in networks with tens of millions of nodes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Opinion Maximization in Social Networks by Modifying Internal Opinions Wang, Gengyu Zhang, Runze Zhang, Zhongzhi Social and Information Networks Data Structures and Algorithms Public opinion governance in social networks is critical for public health campaigns, political elections, and commercial marketing. In this paper, we addresse the problem of maximizing overall opinion in social networks by strategically modifying the internal opinions of key nodes. Traditional matrix inversion methods suffer from prohibitively high computational costs, prompting us to propose two efficient sampling-based algorithms. Furthermore, we develop a deterministic asynchronous algorithm that exactly identifies the optimal set of nodes through asynchronous update operations and progressive refinement, ensuring both efficiency and precision. Extensive experiments on real-world datasets demonstrate that our methods outperform baseline approaches. Notably, our asynchronous algorithm delivers exceptional efficiency and accuracy across all scenarios, even in networks with tens of millions of nodes. |
| title | Opinion Maximization in Social Networks by Modifying Internal Opinions |
| topic | Social and Information Networks Data Structures and Algorithms |
| url | https://arxiv.org/abs/2510.17226 |