This paper investigates causal influences between agents linked by a social graph and interacting over time. In particular, the work examines the dynamics of social learning models and distributed decision-making protocols, and derives expressions that reveal the causal relations between pairs of agents and explain the flow of influence over the network. The results turn out to be dependent on the graph topology and the level of information that each agent has about the inference problem they are trying to solve. Using these conclusions, the paper proposes an algorithm to rank the overall influence between agents to discover highly influential agents. It also provides a method to learn the necessary model parameters from raw observational data. The results and the proposed algorithm are illustrated by considering both synthetic data and real Twitter data.

翻译：本文研究了通过社交图连接并随时间交互的智能体之间的因果影响。具体而言，工作考察了社交学习模型和分布式决策协议的动态过程，推导出揭示智能体对之间因果关系的表达式，并解释了网络中的影响力流动机制。研究结果表明，这些影响依赖于图拓扑结构以及每个智能体对其试图解决的推理问题所掌握的信息水平。基于这些结论，本文提出了一种算法来对智能体之间的整体影响力进行排序，从而发现具有高度影响力的智能体。此外，还提供了一种从原始观测数据中学习必要模型参数的方法。通过合成数据和真实Twitter数据对所提算法及结果进行了验证。