论文标题
在大规模动态社交网络中学习隐藏的影响：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
论文作者
论文摘要
经验数据中的人际影响估计是对社会结构和动态研究的核心挑战。意见动力学理论是一门年轻的跨学科科学，研究了社交网络中的意见形成，并且在应用程序，广告和建议等应用中具有巨大的潜力。 社会影响一词是指由于社会体系中与他人的互动，例如组织，社区或社会一般。 互联网的出现使大量数据很容易获得，可用于衡量对大量人群的社会影响。在这里，我们旨在使用系统和控制观点从数据中从数据中推断出社会影响。首先，我们介绍了一些观点动态的定义和模型，并根据稀疏性概念回顾了在线社交网络的一些结构性约束。然后，我们从一组观察到的数据中回顾了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，重点是样本复杂性和计算要求。
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.
