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Activity Number: 123 - New Challenges and Opportunities in Nonparametric Statistics
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #305369
Title: Temporal Exponential-Family Random Graph Models with Time-Evolving Latent Block Structure for Dynamic Networks
Author(s): Kevin Lee* and Amal Agarwal and Lingzhou Xue
Companies: Western Michigan University and The Pennsylvania State University and Penn State University and National Institute of Statistical Sciences
Keywords: Dynamic networks; Temporal exponential-family random graph model; Hidden Markov model; Variational inference; Model selection

Model-based clustering of dynamic networks has emerged as an essential research topic in statistical network analysis. It is critical to effectively and efficiently model the time-evolving latent block structure of dynamic networks in practice. 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. We prove the identification conditions for both network parameters and transition matrix in our proposed model-based clustering. We propose an effective model selection criterion based on the integrated classification likelihood to choosing an appropriate number of clusters. We develop a variational expectation-maximization algorithm to solve the approximate maximum likelihood estimate. The numerical performance of our proposed method is demonstrated in simulation studies and real data applications.

Authors who are presenting talks have a * after their name.

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