社交互动对学习图的影响
Learning Graph Influence from Social Interactions
摘要：
在社会学习中，智能体通过交换局部信息来形成他们对某些假设的观点或信念。这项工作考虑了最近的弱图的范例，其中网络被划分为发送和接收组件，前者有可能对后者施加支配性的影响。这种图形结构在社交平台上很普遍。我们将不关注直接社会学习问题（考察代理人学习什么），而是关注双重或反向学习问题（考察代理人如何学习）。具体来说，通过对某些代理的信念流的观察，我们想研究是否有可能从网络中发送组件到这些接收代理的连接（影响）的强度。
英文摘要：
In social learning, agents form their opinions or beliefs about certain hypotheses by exchanging local information. This work considers the recent paradigm of weak graphs, where the network is partitioned into sending and receiving components, with the former having the possibility of exerting a domineering effect on the latter. Such graph structures are prevalent over social platforms. We will not be focusing on the direct social learning problem (which examines what agents learn), but rather on the dual or reverse learning problem (which examines how agents learned). Specifically, from observations of the stream of beliefs at certain agents, we would like to examine whether it is possible to learn the strength of the connections (influences) from sending components in the network to these receiving agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
