The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over the graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the graph topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents by using their public tweets (posts).

翻译：自适应社会学习范式有助于建模网络化代理如何在变化环境中对自然状态形成意见并跟踪其漂移。在该框架下，代理基于私有观测反复更新其信念，并与邻居交换信念。本文展示了随时间公开交换的信念序列如何让用户发现关于底层网络拓扑结构及图上信息流的丰富信息。特别地，证明了可以实现：（i）识别每个个体代理对真实学习目标的影响力；（ii）发现每个代理的信息掌握程度；（iii）量化代理之间的成对影响力；（iv）学习底层网络拓扑结构。本文推导的算法还能够在非平稳环境下工作，其中自然状态或图拓扑结构允许随时间漂移。我们将所提算法应用于Twitter用户的不同子网络，并通过其公开推文（帖子）识别出最具影响力和中心性的代理。