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Clustering Time-Evolving Bipartite Networks

Author
Zhang, Yinqi
Published
[University Park, Pennsylvania] : Pennsylvania State University, 2020.
Physical Description
1 electronic document
Additional Creators
Xue, Lingzhou and Schreyer Honors College
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Open Access.
Summary
Network science has emerged as one of the increasingly important research areas. Both generic and bipartite network structures are present in many real-world data in biological science, business, engineering, medical field, etc. In order to understand how network structures emerge and evolve over time, it is critical to carry out efficient estimation and make inferences of network parameters. The estimation and inference work can be accomplished by model-based clustering using exponential-family random graph models (ERGMs). In addition to ERGMs, we incorporate stochastic block models (SBMs) to estimate cluster membership for both static and dynamic bipartite networks. We further develop a variational expectation-maximization (VEM) algorithm to approximate the maximum likelihood of a bipartite network. The thesis will have an in-depth discussion of network analysis and its sub-fields, model-based clustering methods, computational algorithms, and numeric simulation.
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Dissertation Note
B.S. Pennsylvania State University 2020.
Technical Details
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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