Machine learning on graphs has recently achieved impressive progress in various domains, including molecular property prediction and chip design. However, benchmarking practices remain fragmented, often relying on narrow, task-specific datasets and inconsistent evaluation protocols, which hampers reproducibility and broader progress. To address this, we introduce GraphBench, a comprehensive benchmarking suite that spans diverse domains and prediction tasks, including node-level, edge-level, graph-level, and generative settings. GraphBench provides standardized evaluation protocols -- with consistent dataset splits and performance metrics that account for out-of-distribution generalization -- as well as a unified hyperparameter tuning framework. Additionally, we benchmark GraphBench using message-passing neural networks and graph transformer models, providing principled baselines and establishing a reference performance. See www.graphbench.io for further details.

翻译：图机器学习近期在分子性质预测与芯片设计等多个领域取得了显著进展。然而，基准测试实践仍处于碎片化状态，通常依赖于狭窄、任务特定的数据集以及不一致的评估协议，这阻碍了研究的可复现性与更广泛的进展。为此，我们推出了GraphBench，一个涵盖多个领域与预测任务的综合性基准测试套件，包括节点级别、边级别、图级别以及生成式场景。GraphBench提供了标准化的评估协议——包含一致的数据集划分以及考虑了分布外泛化能力的性能指标——以及一个统一的超参数调优框架。此外，我们使用消息传递神经网络与图Transformer模型对GraphBench进行了基准测试，提供了原则性的基线并建立了参考性能指标。更多详情请访问 www.graphbench.io。