Graphon Cross-Validation with Application to Drug Repurposing
Visiting speaker
Huimin Cheng
Assistant Professor, Department of Biostatistics, Boston University
Past Talk
Hybrid talk
Friday
Feb 9, 2024
Watch video
2:30 pm
EST
Virtual
177 Huntington Ave.
11th floor
11th floor
Devon House
58 St Katharine's Way
London E1W 1LP, UK
58 St Katharine's Way
London E1W 1LP, UK
Graphon, short for graph function, provides a generative model for a network. In recent decades, various methods for graphon estimation have been proposed. The success of most graphon estimation methods depends on a proper specification of hyperparameters. Some network cross-validation methods have been proposed, but they suffer from restrictive model assumptions, expensive computational costs, and a lack of theoretical guarantees. To address these issues, we propose a graphon cross-validation (GraphonCV) method. Asymptotic properties of the GraphonCV are established. The effectiveness of the proposed method in terms of both computation and accuracy is demonstrated by extensive simulation studies and real drug repurposing examples
About the speaker
About the speaker
Dr. Huimin Cheng is an Assistant Professor in the Department of Biostatistics at Boston University. She is also affiliated with the Rafik B. Hariri Institute for Computing and Computational Science Engineering at Boston University. She received her PhD in statistics from the University of Georgia in 2023. Her methodological research focuses on statistical network analysis, graph deep learning, causal inference, machine learning, and Riemannian geometry. She modeled the generating process of a network from both non-parametric (e.g., graphon model) and parametric (e.g., SBM) perspectives. She has developed various methods, including network cross-validation, network sampling, network ANOVA, and graphon convolutional network.
Dr. Huimin Cheng is an Assistant Professor in the Department of Biostatistics at Boston University. She is also affiliated with the Rafik B. Hariri Institute for Computing and Computational Science Engineering at Boston University. She received her PhD in statistics from the University of Georgia in 2023. Her methodological research focuses on statistical network analysis, graph deep learning, causal inference, machine learning, and Riemannian geometry. She modeled the generating process of a network from both non-parametric (e.g., graphon model) and parametric (e.g., SBM) perspectives. She has developed various methods, including network cross-validation, network sampling, network ANOVA, and graphon convolutional network.
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