No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets

TL;DR

This paper introduces a principled framework for evaluating graph-learning datasets by analyzing their structure and features through mode-perturbation, aiming to improve benchmarking practices in graph learning.

Contribution

It proposes a novel mode-perturbation framework called Rings and introduces two measures, performance separability and mode complementarity, for systematic dataset evaluation.

Findings

Framework effectively assesses dataset quality through ablation studies.

Experimental results highlight the importance of dataset properties for benchmarking.

Recommendations improve the robustness of graph-learning evaluations.

Abstract

Benchmark datasets have proved pivotal to the success of graph learning, and good benchmark datasets are crucial to guide the development of the field. Recent research has highlighted problems with graph-learning datasets and benchmarking practices -- revealing, for example, that methods which ignore the graph structure can outperform graph-based approaches. Such findings raise two questions: (1) What makes a good graph-learning dataset, and (2) how can we evaluate dataset quality in graph learning? Our work addresses these questions. As the classic evaluation setup uses datasets to evaluate models, it does not apply to dataset evaluation. Hence, we start from first principles. Observing that graph-learning datasets uniquely combine two modes -- graph structure and node features --, we introduce Rings, a flexible and extensible mode-perturbation framework to assess the quality of graph-learning datasets based on dataset ablations -- i.e., quantifying differences between the original dataset and its perturbed representations. Within this framework, we propose two measures -- performance separability and mode complementarity -- as evaluation tools, each assessing the capacity of a graph dataset to benchmark the power and efficacy of graph-learning methods from a distinct angle. We demonstrate the utility of our framework for dataset evaluation via extensive experiments on graph-level tasks and derive actionable recommendations for improving the evaluation of graph-learning methods. Our work opens new research directions in data-centric graph learning, and it constitutes a step toward the systematic evaluation of evaluations.