Multiagent Social Influence: Modeling Persuasion in Contested Social Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917532597747712 |
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| author | Tumu, Renukanandan Vasile, Cristian Ioan Preciado, Victor Mangharam, Rahul |
| author_facet | Tumu, Renukanandan Vasile, Cristian Ioan Preciado, Victor Mangharam, Rahul |
| contents | We present the Social Influence Game (SIG), a framework for modeling adversarial persuasion in social networks with an arbitrary number of competing players. Our goal is to provide a tractable and interpretable model of contested influence that scales to large systems while capturing the structural leverage points of networks. Each player allocates influence from a fixed budget to steer opinions that evolve under DeGroot dynamics, and we prove that the resulting optimization problem is a difference-of-convex program. To enable scalability, we develop an Iterated Linear (IL) solver that approximates player objectives with linear programs. In experiments on random and archetypical networks, IL achieves solutions within 7% of nonlinear solvers while being over 10x faster, scaling to large social networks. This paper lays a foundation for asymptotic analysis of contested influence in complex networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multiagent Social Influence: Modeling Persuasion in Contested Social Networks Tumu, Renukanandan Vasile, Cristian Ioan Preciado, Victor Mangharam, Rahul Social and Information Networks Systems and Control We present the Social Influence Game (SIG), a framework for modeling adversarial persuasion in social networks with an arbitrary number of competing players. Our goal is to provide a tractable and interpretable model of contested influence that scales to large systems while capturing the structural leverage points of networks. Each player allocates influence from a fixed budget to steer opinions that evolve under DeGroot dynamics, and we prove that the resulting optimization problem is a difference-of-convex program. To enable scalability, we develop an Iterated Linear (IL) solver that approximates player objectives with linear programs. In experiments on random and archetypical networks, IL achieves solutions within 7% of nonlinear solvers while being over 10x faster, scaling to large social networks. This paper lays a foundation for asymptotic analysis of contested influence in complex networks. |
| title | Multiagent Social Influence: Modeling Persuasion in Contested Social Networks |
| topic | Social and Information Networks Systems and Control |
| url | https://arxiv.org/abs/2510.01481 |