Graph Mining: Discovering Context-Sensitive Impact and Influence in Complex Systems

Project introduction

Successfully tackling many urgent challenges in socio-economically critical domains (such as sustainability, public health, and biology) requires obtaining a deeper understanding of complex relationships and interactions among a diverse spectrum of entities and agents in different contexts.
While some of the relationships (e.g., co-location of energy production facilities and water delivery networks in the energy domain and scheduled flights between two cities in the study of epidemics) in these domains are explicitly known, the knowledge of these explicit relationships is often far from sufficient to enable decision making. What is required instead is an understanding of whether and (if so) in what contexts these entities impact each other.
The goal of this project is to establish the theoretical, algorithmic, and computational foundations of big data-driven Context-Sensitive Impact Discovery (CSID) in complex systems.

Publications

People involved in this project

  • Dr. Jiannong Xu, Biology, jxu@nmsu.edu, faculty collaborator
  • Dr. David Dubois, Plant and Environmental Sciences, faculty collaborator
  • Dr. Colby Brungard, Plant and Environmental Sciences, faculty collaborator
  • Dr. Chuan Hu, Computer Science, graduate student (graduated in May 2017)
  • Dr. Dong Pei, Biolgoy, graduate student (graduated in May 2017)
  • Dr. Jinjin Jiang, Biology, graduate student (graduated in May 2017)
  • Mr. Nathan Lopez-Brody, Plant and Environmental Sciences, Master’s student (graduated in May 2017)
  • Mr. Brett Pelkey, Computer Science, undergraduate student (graduated in May 2017)
  • Mr. Josue Gutierrez, Mechanical Engineering, undergraduate student

Thanks

This work has been supported by NSF# 1633330. nfs-logo