Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs

TL;DR

This paper critically evaluates the effectiveness of graph classification benchmarks in distinguishing GNN performance, proposing new metrics and methods to better assess dataset quality and guide future benchmark development.

Contribution

It introduces a novel metric for dataset effectiveness, an empirical benchmarking protocol, and a synthetic dataset generation technique to improve graph learning benchmarks.

Findings

Existing benchmarks often fail to distinguish GNNs from simple methods.

The proposed metric aligns with intuitive and prior findings on dataset complexity.

Intrinsic graph properties influence dataset effectiveness and can be manipulated for better benchmarks.

Abstract

Graph classification benchmarks, vital for assessing and developing graph neural networks (GNNs), have recently been scrutinized, as simple methods like MLPs have demonstrated comparable performance. This leads to an important question: Do these benchmarks effectively distinguish the advancements of GNNs over other methodologies? If so, how do we quantitatively measure this effectiveness? In response, we first propose an empirical protocol based on a fair benchmarking framework to investigate the performance discrepancy between simple methods and GNNs. We further propose a novel metric to quantify the dataset effectiveness by considering both dataset complexity and model performance. To the best of our knowledge, our work is the first to thoroughly study and provide an explicit definition for dataset effectiveness in the graph learning area. Through testing across 16 real-world datasets, we found our metric to align with existing studies and intuitive assumptions. Finally, we explore the causes behind the low effectiveness of certain datasets by investigating the correlation between intrinsic graph properties and class labels, and we developed a novel technique supporting the correlation-controllable synthetic dataset generation. Our findings shed light on the current understanding of benchmark datasets, and our new platform could fuel the future evolution of graph classification benchmarks.