我们提出了社会影响博弈（Social Influence Game, SIG），这是一个用于建模具有任意数量竞争参与者的社会网络中对抗性说服过程的框架。我们的目标是提供一个可处理且可解释的 contested influence 模型，该模型既能扩展至大规模系统，又能捕捉网络的结构性杠杆点。各参与者从固定预算中分配影响力，以引导在 DeGroot 动力学下演化的意见；我们证明由此产生的优化问题是差凸规划（difference-of-convex program）。为提升可扩展性，我们开发了一种迭代线性（Iterated Linear, IL）求解器，通过线性规划近似各参与者的优化目标。在随机网络与典型网络的实验中，IL 求解器所得解与非线性求解器结果相差在 7% 以内，同时速度提升逾 10 倍，可扩展至大规模社会网络。本文为复杂网络中 contested influence 的渐近分析奠定了基础。

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.