Avanti Athreya, “Discovering underlying dynamics in time series of networks”
In-person in Clark Hall, Room 110
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virtually over Zoom
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https://wse.zoom.us/j/97055652302?pwd=dWFUUHRHS1lna2h5K0U1cEt4RDRrQT09
Avanti Athreya, PhD
Associate Research Professor
Johns Hopkins University
“Discovering underlying dynamics in time series of networks”
Abstract: Understanding dramatic changes in the evolution of networks is central to statistical network inference. We consider a joint network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the time-varying evolution of the constellation of latent vectors exhibits a low-dimensional manifold structure under a suitable notion of distance. This distance can be approximated by a measure of separation between the observed networks themselves, and there exist Euclidean representations for underlying network structure, as characterized by this distance, at any given time. These Euclidean representations and their data-driven estimates permit the visualization of network evolution and transform network inference questions such as change-point and anomaly detection into a classical setting. We illustrate our methodology with real and synthetic data, and identify change points corresponding to shifts in pandemic policies in a communication network of a large organization.
Biography: Avanti Athreya is an associate research professor in the Department of Applied Mathematics and Statistics. Her work focuses on probability and stochastic processes.