STA 290 Seminar Series
Thursday, January 7th, 4:10pm, MSB 1147 (Colloquium Room)
Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)
Speaker: Chao Gao (Yale University)
Title: “Network analysis: community detection and graphon estimation”
Abstract: Network data analysis is an important area in modern statistics. How to do community detection and parameter estimation in provably optimal ways are two key questions in analyzing network data. In this talk, I will present results in both problems. I will first introduce an efficient two-step polynomial-time algorithm that can achieve the optimal misclassification error for community detection. The procedure consists of a novel spectral initialization step and a majority voting refinement step. I will then formulate the problem of network parameter estimation as nonparametric graphon estimation, and establish its link to nonparametric regression without observing design. The minimax rate of graphon estimation consists of two parts: the nonparametric part and the clustering part. An interesting implication is that the smoothness of the graphon does not affect the minimax rate once it is greater than 1.