Statistics Colloquium
September 20, 11 a.m.
Alavi Commons Room, 6625 Everett Tower

Clustering Time-Evolving Networks through Temporal ERGMs with a Dynamic Latent Block Structure

Kevin Lee
Department of Statistics

Model-based clustering of dynamic networks has emerged as an essential research topic in statistical network analysis. We present a principled statistical clustering of dynamic networks through the temporal exponential-family random graph models with a hidden Markov structure. The temporal exponential-family random graph models allow us to detect groups based on interesting features of the dynamic networks and the hidden Markov structure is used to infer the time-evolving block structure of dynamic networks. The power of our proposed method is demonstrated in real-world applications.

Kevin Lee is Assistant Professor in the Department of Statistics at Western Michigan University and advisor of the Data Science program. He received his Ph.D. from Penn State in 2017. His research interests include analysis of network data, high-dimensional statistical inference, machine learning and data mining, graphical models, and variational inference.

All statistics graduate students are expected to attend.


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Department of Statistics
3304 Everett Tower
Western Michigan University
Kalamazoo MI 49008-5152 USA
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