Inferring Network Structure and Flows Using Partial Observations

Predicting network structure and flows on edges is a precondition to effective planning and disaster response in critical infrastructure networks. Accordingly, we propose to develop algorithms inspired by network science, physical modeling, and machine learning to determine this information from incomplete observations. Using both physical domain-specific and data-driven approaches, these algorithms will address diverse problems including: the reconstruction of network topology and parameters, the estimation of network flows, the optimal selection and scheduling of multiple types of sensors, and the modeling of interdependencies in multilayer networks. Together these methods will develop a rigorous theory  or solving the inverse problem of edge and flow determination from observations of critical infrastructure networks.

Affiliated People

Photo of Zexi Huang.
Research interests: 

Graph Data Mining, Representation Learning

Zexi received his B.Eng. in Computer Science and Technology at University of Electronic Science and Technology of China, Chengdu in 2018. He joined Dynamo lab in 2018. His research interests span the analysis of social, informational, and biological networks with machine learning and data mining techniques. 

Research interests: 

Machine Learning, Data Mining

Furkan received his B.Sc. Computer Science & Engineering with a Minor in Economics from Bilkent University, Ankara, in 2016.