学习大规模动态社交网络中的隐藏影响：基于数据驱动的稀疏方法
Learning hidden influences in large-scale dynamical social networks: A data-driven sparsity-based approach
摘要：
从经验数据中估计人际影响力是社会结构和动态研究中的一个核心挑战。意见动力学理论是一门研究社交网络中意见形成的新兴交叉学科，在市场营销、广告和推荐等方面有着巨大的应用潜力。社会影响力是指个体在社会系统中与他人的相互作用所引起的行为变化，例如组织，社区或一般社会。互联网的出现使大量数据易于获得，可用于衡量社会对大量人口的影响。在这里，我们的目标是定性和定量地推断社会影响力的数据使用系统和控制的观点。首先，我们介绍了一些意见动力学的定义和模型，并回顾了在线社交网络的结构约束，基于稀疏性的概念。然后，我们回顾了从一组观测数据推断网络结构的主要方法。最后，我们提出了一些算法，利用引入的模型和结构约束，重点放在样本的复杂性和计算要求。
英文摘要：
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.
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