Counting Triangles in Real-World Graph Streams: Dealing with Repeated Edges and Time Windows

TL;DR

This paper introduces a novel streaming algorithm for accurately estimating triangle counts in multigraph streams, effectively handling duplicate edges and multiple time windows with theoretical guarantees and strong empirical results.

Contribution

The authors develop a debiased triangle counting algorithm for multigraph streams that overcomes limitations of previous methods designed for simple graphs, supporting multiple time windows without prior specification.

Findings

The algorithm achieves high accuracy in real-world graph streams.

It effectively handles duplicate edges without extra memory overhead.

The method reveals new insights into temporal graph transitivity trends.

Abstract

Real-world graphs often manifest as a massive temporal stream of edges. The need for real-time analysis of such large graph streams has led to progress on low memory, one-pass streaming graph algorithms. These algorithms were designed for simple graphs, assuming an edge is not repeated in the stream. Real graph streams however, are almost always multigraphs i.e., they contain many duplicate edges. The assumption of no repeated edges requires an extra pass *storing all the edges* just for deduplication, which defeats the purpose of small memory algorithms. We describe an algorithm for estimating the triangle count of a multigraph stream of edges. We show that all previous streaming algorithms for triangle counting fail for multigraph streams, despite their impressive accuracies for simple graphs. The bias created by duplicate edges is a major problem, and leads these algorithms astray. Our algorithm avoids these biases through careful debiasing strategies and has provable theoretical guarantees and excellent empirical performance. Our algorithm builds on the previously introduced wedge sampling methodology. Another challenge in analyzing temporal graphs is finding the right temporal window size. Our algorithm seamlessly handles multiple time windows, and does not require committing to any window size(s) a priori. We apply our algorithm to discover fascinating transitivity and triangle trends in real-world graph streams.