Multiagent Social Influence: Modeling Persuasion in Contested Social Networks
Provides a tractable and scalable model for contested influence in social networks, relevant to researchers studying opinion dynamics and influence maximization.
The authors introduce the Social Influence Game (SIG) for modeling adversarial persuasion in social networks with multiple competing players. Their Iterated Linear solver achieves solutions within 7% of nonlinear solvers while being over 10x faster, scaling to large networks.
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.