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 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 network 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 merely by using their public tweets (posts).

翻译：适应性社会学习模式有助于模拟网络代理人如何就自然状态形成意见并跟踪其在变化环境中的漂移情况。在这个框架内,代理人根据私人观察反复更新其信仰,并与邻居交换信仰。在这项工作中,可以显示公众交流信仰的顺序如何使用户能够发现关于基本网络地形和信息流的丰富信息,特别是显示:(一) 查明每个代理人对真相学习目标的影响,(二) 查明每个代理人对真相学习目标的了解程度,(三) 量化代理人之间的对称影响,(四) 学习基本网络地形。这里得出的算法还可以在非静止环境中工作,在这种环境中,自然的真实状况或网络地形可以随时间移动。我们将提议的算法应用于不同子网络的推特用户,并仅仅通过使用其公开的推文(poss)来识别最有影响力和最核心的代理人。