Event Date
SPEAKER: Can Le; Assistant Professor, Dept of Statistics, UC Davis
TITLE: “Estimating a network from multiple noisy realizations”
ABSTRACT: Complex interactions between entities are often represented as edges in a network. In practice, the network is often constructed from noisy measurements and inevitably contains some errors. In this talk we consider the problem of estimating a network from multiple noisy observations where edges of the original network are recorded with both false positives and false negatives. The key to optimally leveraging these multiple observations is to take advantage of network structure, and here we focus on the case where the true network contains communities. Under a community structure assumption on the truth, we derive an efficient method to estimate the noise levels and the original network, with theoretical guarantees on the convergence of our estimates. If time permits, we will also discuss some network sampling and inference problems.
DATE: Thursday, April 11th, 4:10pm
LOCATION: MSB 1147, Colloquium Room
REFRESHMENTS: 3:30pm MSB 4110 (4th floor lounge)
STA 290 Seminar List: https://statistics.ucdavis.edu/seminars