Sampling and Estimation for (Sparse) Exchangeable Graphs

TL;DR

This paper extends the empirical graphon estimation method from dense to sparse exchangeable graphs using the graphex framework, introducing a consistent estimator and a natural sampling scheme for sparse network analysis.

Contribution

It develops a new sampling scheme and a dilation-based estimator for sparse exchangeable graphs within the graphex framework, generalizing dense graph estimation techniques.

Findings

Proposes a natural sampling scheme for sparse graphs.

Introduces a consistent estimator for the underlying graphex.

Extends empirical graphon estimation to sparse graph regimes.

Abstract

Sparse exchangeable graphs on $\mathbb{R}_+$, and the associated graphex framework for sparse graphs, generalize exchangeable graphs on $\mathbb{N}$, and the associated graphon framework for dense graphs. We develop the graphex framework as a tool for statistical network analysis by identifying the sampling scheme that is naturally associated with the models of the framework, and by introducing a general consistent estimator for the parameter (the graphex) underlying these models. The sampling scheme is a modification of independent vertex sampling that throws away vertices that are isolated in the sampled subgraph. The estimator is a dilation of the empirical graphon estimator, which is known to be a consistent estimator for dense exchangeable graphs; both can be understood as graph analogues to the empirical distribution in the i.i.d. sequence setting. Our results may be viewed as a generalization of consistent estimation via the empirical graphon from the dense graph regime to also include sparse graphs.