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

NetSE：小型：现实世界中的社交网络：从感知到结构分析

• 批准号: 1017525
• 负责人: Sujay Sanghavi
• 金额: $ 50万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017525&HistoricalAwards=false
• 关键词: NetSE; Small; Social; Networks; Real

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.

在线社交网络现在提供了一种社交互动的视图，这种视图在规模、粒度和--同样重要的--对自动分析的顺从性方面是无与伦比的。然而，我们的社会资本中只有一小部分花在了网上;此外，在线网络通常只是更丰富的因果关系网络的一个反映，这些网络支配着我们的日常互动--关系通常首先是在线发展起来的。这项提议的目的是将自动化数据驱动理解的全部力量应用于真实的世界中的社交网络--这可能更重要。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协同定位，并通过马尔可夫随机场中的结构学习理论从单纯的协同定位中提取真正的交互。该研究将表征网络属性（如子组，聚类和小世界）和节点属性（如中心性和一致性），并通过具有顺序观察的压缩感知理论捕获参与者交互的自然演变。这项提案中开创的技术将大大提高我们对真实的世界中的社交网络进行有意义的数据驱动理解的能力。行业互动将从一开始就通过UT Austin建立的行业合作伙伴关系为这项研究提供信息。学生的参与，无论是本科生和研究生，在于本研究的核心，提供了一个自然的场地，介绍代表性不足的群体网络研究。社交网络提供了一个自然吸引人的主题，向高中生介绍工程和网络，PI将通过在地区学校的讲座来完成。