A note on approximating the average degree of bounded arboricity graphs

TL;DR

This paper revisits a simple, previously analyzed algorithm for approximating the average degree of graphs with bounded arboricity, providing a clearer presentation and improved query complexity bounds.

Contribution

It offers a full, simplified presentation of an existing algorithm for average degree approximation in bounded arboricity graphs, with refined analysis and query complexity bounds.

Findings

Achieves a $(1+\varepsilon)$-approximation of average degree with $O(rac{ ext{ extalpha}}{ ext{d} ext{ extepsilon}^2})$ queries.

Provides a modified version with $O(rac{ ext{ extalpha}}{ ext{ extepsilon}^2}\sqrt{rac{n}{d}})$ queries.

Clarifies the algorithm's analysis and removes logarithmic losses present in earlier descriptions.

Abstract

Estimating the average degree of graph is a classic problem in sublinear graph algorithm. Eden, Ron, and Seshadhri (ICALP 2017, SIDMA 2019) gave a simple algorithm for this problem whose running time depended on the graph arboricity, but the underlying simplicity and associated analysis were buried inside the main result. Moreover, the description there loses logarithmic factors because of parameter search. The aim of this note is to give a full presentation of this algorithm, without these losses. Consider standard access (vertex samples, degree queries, and neighbor queries) to a graph $G = (V,E)$ of arboricity at most $\alpha$. Let $d$ denote the average degree of $G$. We describe an algorithm that gives a $(1+\varepsilon)$-approximation to $d$ degree using $O(\varepsilon^{-2}\alpha/d)$ queries. For completeness, we modify the algorithm to get a $O(\varepsilon^{-2} \sqrt{n/d})$ query