Network resampling for estimating uncertainty

TL;DR

This paper explores three network resampling methods to estimate uncertainty in network analysis, proposing algorithms for confidence interval construction and sampling fraction selection, with empirical evaluation on simulated and real Facebook data.

Contribution

It introduces a general framework for network resampling to estimate uncertainty and algorithms for optimal sampling fraction selection, addressing a gap in network analysis methods.

Findings

No single resampling method is best for all tasks.

Selecting an appropriate sampling fraction improves uncertainty estimation.

Algorithms perform well on both simulated and real network data.

Abstract

With network data becoming ubiquitous in many applications, many models and algorithms for network analysis have been proposed. Yet methods for providing uncertainty estimates in addition to point estimates of network parameters are much less common. While bootstrap and other resampling procedures have been an effective general tool for estimating uncertainty from i.i.d. samples, adapting them to networks is highly nontrivial. In this work, we study three different network resampling procedures for uncertainty estimation, and propose a general algorithm to construct confidence intervals for network parameters through network resampling. We also propose an algorithm for selecting the sampling fraction, which has a substantial effect on performance. We find that, unsurprisingly, no one procedure is empirically best for all tasks, but that selecting an appropriate sampling fraction substantially improves performance in many cases. We illustrate this on simulated networks and on Facebook data.