Replication Data for: DECOMPOSING NETWORK INFLUENCE: SOCIAL INFLUENCE REGRESSION

Understanding network influence and its determinants are key challenges in political science and network analysis. Traditional latent variable models position actors within a social space based on network dependencies but often do not elucidate the underlying factors driving these interactions. To overcome this limitation, we propose the Social Influence Regression (SIR) model, an extension of vector autoregression tailored for relational data that incorporates exogenous covariates into the estimation of influence patterns. The SIR model captures influence dynamics via a pair of n × n matrices that quantify how the actions of one actor affect the future actions of another. This framework not only provides a statistical mechanism for explaining actor influence based on observable traits but also improves computational efficiency through an iterative block coordinate descent method. We showcase the SIR model’s capabilities by applying it to monthly conflict events between countries, using data from the Integrated Crisis Early Warning System (ICEWS). Our findings demonstrate the SIR model’s ability to elucidate complex influence patterns within networks by linking them to specific covariates. This paper’s main contributions are: (1) introducing a model that explains thirdorder dependencies through exogenous covariates and (2) offering an efficient estimation approach that scales effectively with large, complex networks.

探析网络影响力及其决定因素，是政治学与网络分析领域的核心研究难题。传统潜变量模型（latent variable model）会基于网络依存关系将行动者嵌入社会空间，但通常无法阐明驱动此类互动的深层影响因素。为克服这一局限，本文提出社会影响力回归（Social Influence Regression, SIR）模型——一种专为关系数据设计的向量自回归（vector autoregression）扩展模型，将外生协变量纳入影响力模式的估计流程。SIR模型通过一对n×n矩阵刻画影响力动态，该矩阵可量化单个行动者的行为对其他行动者后续行为的影响强度。该框架不仅为基于可观测特征解释行动者影响力提供了统计学机制，还通过迭代块坐标下降法提升了计算效率。本文采用综合危机预警系统（Integrated Crisis Early Warning System, ICEWS）的公开数据集，将SIR模型应用于国家间月度冲突事件分析，以此展示模型的性能与适用性。研究结果显示，SIR模型可通过将网络内的复杂影响力模式与特定协变量关联，清晰阐明此类模式的内在逻辑。本文的核心贡献有二：其一，提出了一种可通过外生协变量解释三阶依存关系的模型；其二，提供了一种可高效适配大规模复杂网络的高效估计算法。