Sampling an Edge in Sublinear Time Exactly and Optimally

TL;DR

This paper presents an optimal algorithm for exactly sampling an edge uniformly in a graph in sublinear time, closing a long-standing open problem and improving previous approximate sampling methods.

Contribution

It introduces a new algorithm that samples an edge uniformly in exactly O(n/√m) time, matching the lower bound and improving upon prior approximate methods.

Findings

Achieves exact uniform edge sampling in O(n/√m) time.

Closes the open problem of optimal sublinear-time edge sampling.

Matches the theoretical lower bound for the problem.

Abstract

Sampling edges from a graph in sublinear time is a fundamental problem and a powerful subroutine for designing sublinear-time algorithms. Suppose we have access to the vertices of the graph and know a constant-factor approximation to the number of edges. An algorithm for pointwise $\varepsilon$-approximate edge sampling with complexity $O(n/\sqrt{\varepsilon m})$ has been given by Eden and Rosenbaum [SOSA 2018]. This has been later improved by T\v{e}tek and Thorup [STOC 2022] to $O(n \log(\varepsilon^{-1})/\sqrt{m})$. At the same time, $\Omega(n/\sqrt{m})$ time is necessary. We close the problem, by giving an algorithm with complexity $O(n/\sqrt{m})$ for the task of sampling an edge exactly uniformly.