B1480
Title: Graphon cross-validation
Authors: Huimin Cheng - Boston University (United States) [presenting]
Yongkai Chen - University of Georgia (United States)
Ping Ma - University of Georgia (United States)
WenXuan Zhong - University of Georgia (United States)
Abstract: Graphon, short for graph function, provides a generative model for networks. 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 suffer from restrictive model assumptions, expensive computational costs, and a lack of theoretical guarantees. To address these issues, a masked mirror validation (GraphonCV) method is proposed. Asymptotic properties of the GraphonCV are established. The proposed method's effectiveness in computation and accuracy is demonstrated by extensive simulation studies and real experiments.
