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

在线社交网络现在提供了一种社交互动的视角，在规模、粒度以及自动化分析的可修饰性方面都是无与伦比的--这一点同样重要。然而，我们的社会资本中只有一小部分花在了网上；此外，在线网络通常只是管理我们日常互动的更丰富、有因果关系的网络的集合，而这些关系通常是由网络发展而来的。这一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--可以说是更重要的社交网络。该研究将开发一个可扩展的基于传感器标签的基础设施，通过将参与者与标签动态配对来测量参与者的并置，并通过马尔可夫随机场的结构学习理论从纯粹的并置中提取真正的交互。这项研究将表征网络属性(如子群、集群和小世界)和节点属性(如中心性和内部)，并通过顺序观测的压缩感知理论捕获参与者交互的自然演化。这一提议中首创的技术将显著提高我们对现实世界中的社交网络获得有意义的数据驱动的理解的能力。行业互动将通过德克萨斯大学奥斯汀分校建立的行业合作伙伴关系，从一开始就为这项研究提供信息。学生参与，包括本科生和研究生，是本研究的核心，为向代表不足的群体介绍网络研究提供了一个自然的场所。社交网络提供了一个自然吸引人的主题，向高中生介绍工程学和网络，PI将通过在地区学校的演讲来做到这一点。

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.