Graph Classification Benchmark

Graph classification benchmarks are crucial for evaluating the performance of graph neural networks (GNNs) and other graph-based machine learning models. Recent research highlights the need for more effective benchmarks that accurately reflect the capabilities of GNNs, focusing on developing metrics to assess dataset quality and exploring novel augmentation techniques like graph mixup and Schur complement-based methods to improve model robustness and generalization. These efforts aim to improve the design and evaluation of GNNs, ultimately leading to more reliable and powerful models for various applications involving graph-structured data, such as drug discovery and social network analysis. The development of new benchmark datasets, particularly those with challenging characteristics like imbalanced classes and large graph sizes, is also a key area of focus.