Revisiting Graph Neural Networks for Graph-level Tasks: Taxonomy, Empirical Study, and Future Directions

arXiv:2501.00773v2 Announce Type: replace-cross
Abstract: Graphs are fundamental data structures for modeling complex interactions in domains such as social networks, molecular structures, and biological systems. Graph-level tasks, which involve predicting properties or labels for entire graphs, are crucial for applications like molecular property prediction and subgraph counting. While Graph Neural Networks (GNNs) have shown significant promise for these tasks, their evaluations are often limited by narrow datasets, task coverage, and inconsistent experimental setups, hindering their generalizability. In this paper, we present a comprehensive experimental study of GNNs on graph-level tasks, systematically categorizing them into five types: node-based, hierarchical pooling-based, subgraph-based, graph learning-based, and self-supervised learning-based GNNs. To address these challenges, we propose a unified evaluation framework OpenGLT for graph-level GNNs. OpenGLT standardizes the evaluation process across diverse datasets, multiple graph tasks (e.g., classification and regression), and real-world scenarios, including noisy, imbalanced, and few-shot graphs. Extensive experiments are conducted on 16 baseline models across five categories, evaluated on 13 graph classification and 13 graph regression datasets. These experiments provide comprehensive insights into the strengths and weaknesses of existing GNN architectures.