Scalable Triangle Counting: The Threshold Algorithm

2026-09-14

AI Summary

本文提出了一种简单的单遍三角形计数算法，通过设定阈值来估计图中三角形数量，无需预先知道总数或固定内存预算，适用于大规模图数据。

Abstract

We study one-pass triangle counting on random-order edge streams. We present a remarkably simple algorithm---read edges from the stream until $Q$ triangles are observed in the prefix, then output $Q\,(m/S)^3$ where $S$ is the stopping length---and prove that, when the maximum number of triangles incident to any edge satisfies $η\le T^{2/3}$, this is a $(1\pm\varepsilon)$-approximation of $T$ with probability $1-δ$ using $O(\varepsilon^{-2}\log(1/δ)\, m/T^{1/3})$ memory. Crucially, the algorithm does not need any a priori estimate of $T$, in sharp contrast with state-of-the-art sampling-rate based algorithms (McGregor and Vorotnikova, PODS 2020; Tsourakakis et al., KDD 2009). It also does not need a prescribed memory budget: the stopping rule self-selects the prefix length and can return an estimate before reading the entire stream. The proof rests on a Schudy--Sviridenko concentration argument for an independent-edge-sampling estimator, coupled to the without-replacement prefix produced by the algorithm. On six real temporal streams, the algorithm's stopping prefix follows the predicted cube-root scaling and achieves at most $6\%$ error at a $10\%$ prefix, without using $T$. At a fixed stored-edge budget, variance-reduced reservoir samplers are often more accurate, but only after reading the entire stream. On a separate, much larger, $1.8\times10^9$-edge graph, the threshold algorithm reads $0.46\%$ of the stream and returns $3.8\%$ error, while the strongest reservoir baselines do not finish a pass within the wall-clock cap.

Problem

• triangle counting
• edge streams
• approximation
• memory budget

Innovation

• one-pass triangle counting
• random-order edge streams
• no a priori estimate of T
• self-selecting prefix length
• concentration argument