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Activity Number: 344
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318797
Title: Model-Based Clustering for Large-Scale Dynamic Networks
Author(s): Kevin Lee* and Lingzhou Xue and David Hunter
Companies: Penn State University and Penn State University and Penn State University
Keywords: Dynamic networks ; Model-based clustering ; Variational method ; EM algorithm ; Model selection ; Collaboration networks

Dynamic network modeling provides an emerging statistical technique to various real-world applications. It is a fundamental research question to detect the communities in large scale dynamic networks. However, due to significant computational challenges and difficulties in modeling communities, there is little progress in finding communities in dynamic networks. We present a novel model-based clustering framework for dynamic networks based on the exponential-family random graph models. We propose an effective model selection criterion to choose the number of communities. By using variational methods and MM techniques, we propose an efficient generalized variational expectation-maximization algorithm to solve approximate maximum likelihood estimates. Our method is demonstrated in an empirical application to the dynamic collaboration network data of a large northeastern research university. Our results provide insights about how different researchers work with their collaborators.

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

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