SAND Lab, Graph Dynamics

Project Abstract

Online social networks (OSNs) such as Facebook and LinkedIn are valuable infrastructures for communication and interactions between a large volume of Internet users. For years, researchers have been trying to answer fundamental questions about the formation of these complex networks, their ongoing evolution, formation of internal structures, and change at different time scales. Since answering these questions requires real dynamics datasets at scale, most prior studies have been significantly constrained by a lack of data. The PIs have been granted access by an OSN provider to a uniquely detailed and complete trace of dynamics over 2+ years of a social network. Our goal is to mine and analyze our traces of network dynamics to validate existing models and guide new models for fine grain network dynamics. Objectives include analysis of the preferential attachment model at different stages of network growth, developing new models of network dynamics at fine granularity in both time and graph topology, and explorations of applications driven by novel metrics of graph dynamics.

Our work has the potential to dramatically change our understanding of dynamics in online social networks. By taking an empirical, data-driven approach to network modeling, we can shed light on how traditional models of network dynamics deviate from ground truth. In addition, we can develop empirical models that are more effective at accurately predicting network events at small scales. Both PIs Zhao and Zheng are heavily invested in educational and outreach programs for female and minority students: female students and postdocs often outnumber male counterparts in their lab. The PIs will disseminate their results to their collaborators at Renren and LinkedIn, and also share results with researchers at Twitter, Zynga, Facebook and Google through existing technical contacts and informal visits/talks.

This project is supported in part by NSF Proposal: IIS-1321083, titled "Analysis and Models of Social Network Structure, Growth and Dynamics."