Interpersonal influence estimation from empirical data is a central challenge in the study of social structures and dynamics. Opinion dynamics theory is a young interdisciplinary science that studies opinion formation in social networks and has a huge potential in applications, such as marketing, advertisement and recommendations. The term social influence refers to the behavioral change of individuals due to the interactions with others in a social system, e.g. organization, community, or society in general. The advent of the Internet has made a huge volume of data easily available that can be used to measure social influence over large populations. Here, we aim at qualitatively and quantitatively infer social influence from data using a systems and control viewpoint. First, we introduce some definitions and models of opinions dynamics and review some structural constraints of online social networks, based on the notion of sparsity. Then, we review the main approaches to infer the network's structure from a set of observed data. Finally, we present some algorithms that exploit the introduced models and structural constraints, focusing on the sample complexity and computational requirements.

中文翻译：

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在大规模动态社交网络中学习隐藏的影响：一种数据驱动的基于稀疏性的方法

从经验数据估计人际影响是社会结构和动态研究的核心挑战。观点动力学理论是一门研究社交网络中观点形成的新兴交叉学科，在市场营销、广告和推荐等领域具有巨大的应用潜力。术语社会影响是指由于与社会系统（例如组织、社区或一般社会）中的其他人的相互作用而导致的个人行为变化。互联网的出现使大量数据变得容易获得，可用于衡量对大量人口的社会影响。在这里，我们的目标是使用系统和控制观点从数据中定性和定量地推断社会影响。第一的，基于稀疏性的概念，我们介绍了意见动态的一些定义和模型，并回顾了在线社交网络的一些结构性约束。然后，我们回顾了从一组观察到的数据中推断网络结构的主要方法。最后，我们提出了一些利用引入的模型和结构约束的算法，重点关注样本复杂性和计算要求。