Benchmarking Graph Neural Networks

TL;DR

This paper introduces an updated benchmark framework for graph neural networks (GNNs), including diverse datasets and tools for fair comparison, which has significantly impacted the GNN research community by enabling systematic evaluation and fostering new insights.

Contribution

The paper presents an improved, open-source GNN benchmarking framework with new datasets and demonstrates its utility through analyzing graph positional encoding techniques.

Findings

The benchmark framework is widely adopted, with over 2,000 GitHub stars.

Inclusion of the AQSOL molecular dataset enhances real-world applicability.

Study of graph positional encoding demonstrates its importance in GNN performance.

Abstract

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas. As of December 2022, the GitHub repository has reached 2,000 stars and 380 forks, which demonstrates the utility of the proposed open-source framework through the wide usage by the GNN community. In this paper, we present an updated version of our benchmark with a concise presentation of the aforementioned framework characteristics, an additional medium-sized molecular dataset AQSOL, similar to the popular ZINC, but with a real-world measured chemical target, and discuss how this framework can be leveraged to explore new GNN designs and insights. As a proof of value of our benchmark, we study the case of graph positional encoding (PE) in GNNs, which was introduced with this benchmark and has since spurred interest of exploring more powerful PE for Transformers and GNNs in a robust experimental setting.