Summary
Many complex systems in the real world can be modeled as networks. In fact, many networks vary over time. For example, contact networks change from one shape to another as people move around to meet different people. Friendship networks also vary over time on a longer timescale. Such temporal (i.e., time-varying) network data have been increasingly available, and mathematically founded methods that can efficiently summarize complex temporal network data to help enhance intuitive understanding of the data are desirable. The Principal Investigator (PI) will develop methods to map temporal networ
