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TITLE: Topological Inference on Large Scale Graphon
ABSTRACT:
We propose to test the topological structures of complex networks under the graphon model. Graphon is a nonparametric model for large scale stochastic graphs. Many works have been done on graphon estimation, however it is not easy to interpret the network structures from estimators. We provide an inferential toolkit to study the persistent homology of the graphon landscape which reveals the clustering structure of stochastic networks. Our methods are applied to the neuroscience data related to visual memories.
Bio: Junwei Lu is a final year Ph.D. student in the Department of Operations Research and Financial Engineering at Princeton University. His researches focus on new inferential methods for modern statistical analysis with complex data structures and complicated algorithms. Junwei Lu has received several academic rewards, including Award for Excellence in Princeton Engineering School, the ICSA best student paper award and ASA Best Student Paper in Nonparametric Statistics.
