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

University Of Texas At Austin, Austin TX

Investigators

Abstract

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.