NetSE: Small: Social Networks in the Real World: From Sensing to Structure Analysis

在线社交网络现在提供的社交互动视图在规模、粒度和同样重要的自动化分析方面都是无与伦比的。然而，我们的社会资本中只有一小部分花在了网上；此外，在线网络通常只是一种改造&amp；#64258；更丰富的因果网络的一部分，支配着我们的日常互动——典型的人际关系&amp；RST开发&amp；#64258；这项提议的目的是将自动化数据驱动的理解的全部力量应用于现实世界中的社交网络——可以说更重要的是——。该研究将开发一个可扩展的基于传感器标签的基础设施，通过动态地将参与者与标签配对来测量参与者的共址，并通过马尔可夫随机场的结构学习理论从单纯的共址中提取真正的交互。该研究将描述网络属性（如子组、聚类和小世界）和节点属性（如中心性和影响），并通过具有顺序观察的压缩感知理论捕捉参与者交互的自然演变。在这个提议中开创的技术将大大提高我们在现实世界中获得有意义的数据驱动的社交网络理解的能力。通过德克萨斯大学奥斯汀分校建立的行业合作伙伴关系，行业互动将从一开始就为这项研究提供信息。本科生和研究生的学生参与是本研究的核心，为将代表性不足的群体引入网络研究提供了一个自然的场所。社交网络为高中生介绍工程和网络提供了一个自然的吸引人的主题，pi将通过在地区学校的讲座来做到这一点。

Online social networks now provide a view of social interactions that is unmatched in scale, granularity and - equally important - amenability to automated analysis. However, only a small fraction of our social capital is spent online; moreover, online networks are typically only a reflection of richer, causative networks that govern our everyday interactions - relationships typically first develop offline. The aim of this proposal is to bring the full power of automated data-driven understanding to bear on the - arguably more important - social networks in the real world. The research will develop a scalable sensor-tag based infrastructure that measures co-locations of participants by dynamically pairing participants with tags and extract genuine interactions from mere colocations via theory of structure learning in Markov Random Fields. The research will characterize network properties (like subgroups, clustering and small worlds), and node properties (like centrality and influence) and capture the natural evolution of participant interactions via the theory of compressed sensing with sequential observations. The techniques pioneered in this proposal will significantly advance our ability to obtain a meaningful data-driven understanding of social networks in the real world. Industry interaction will inform this research from the beginning, via established industry partnerships at UT Austin. Student involvement, both undergraduate and graduate, lies at the core of this research, providing a natural venue to introduce under-represented groups to networking research. Social networks provide a naturally engaging subject to introduce high-school students to engineering and networks, which the PIs will do via talks in area schools.