no code implementations • ICML 2020 • Qiaohui Lin, Robert Lunde, Purnamrita Sarkar
We study the properties of a leave-node-out jackknife procedure for network data.
no code implementations • 14 Sep 2020 • Qiaohui Lin, Robert Lunde, Purnamrita Sarkar
We propose a new class of multiplier bootstraps for count functionals, ranging from a fast, approximate linear bootstrap tailored to sparse, massive graphs to a quadratic bootstrap procedure that offers refined accuracy for smaller, denser graphs.
no code implementations • NeurIPS 2021 • Robert Lunde, Purnamrita Sarkar, Rachel Ward
We consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming principal component analysis, where the data are generated IID from some unknown distribution.
no code implementations • 20 Feb 2023 • Robert Lunde, Elizaveta Levina, Ji Zhu
An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics.
no code implementations • 12 Jun 2023 • Robert Lunde
We study the properties of conformal prediction for network data under various sampling mechanisms that commonly arise in practice but often result in a non-representative sample of nodes.