GraphWorld: Fake Graphs Bring Real Insights for GNNs

TL;DR

GraphWorld introduces a scalable, flexible system for benchmarking GNNs on a vast array of synthetic graphs, providing deeper insights into model performance and graph property relationships beyond traditional datasets.

Contribution

This work presents GraphWorld, a novel system for generating and benchmarking GNNs on large, diverse synthetic graph datasets, enhancing evaluation robustness and insight.

Findings

Revealed new performance patterns of GNNs across diverse synthetic datasets.

Uncovered relationships between graph properties and GNN task performance.

Demonstrated the scalability and accessibility of GraphWorld for extensive benchmarking.

Abstract

Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets are currently used to evaluate new models. This continued reliance on a handful of datasets provides minimal insight into the performance differences between models, and is especially challenging for industrial practitioners who are likely to have datasets which look very different from those used as academic benchmarks. In the course of our work on GNN infrastructure and open-source software at Google, we have sought to develop improved benchmarks that are robust, tunable, scalable,and generalizable. In this work we introduce GraphWorld, a novel methodology and system for benchmarking GNN models on an arbitrarily-large population of synthetic graphs for any conceivable GNN task. GraphWorld allows a user to efficiently generate a world with millions of statistically diverse datasets. It is accessible, scalable, and easy to use. GraphWorld can be run on a single machine without specialized hardware, or it can be easily scaled up to run on arbitrary clusters or cloud frameworks. Using GraphWorld, a user has fine-grained control over graph generator parameters, and can benchmark arbitrary GNN models with built-in hyperparameter tuning. We present insights from GraphWorld experiments regarding the performance characteristics of tens of thousands of GNN models over millions of benchmark datasets. We further show that GraphWorld efficiently explores regions of benchmark dataset space uncovered by standard benchmarks, revealing comparisons between models that have not been historically obtainable. Using GraphWorld, we also are able to study in-detail the relationship between graph properties and task performance metrics, which is nearly impossible with the classic collection of real-world benchmarks.