A new study introduces a reproducible graph neural network (GNN) benchmarking framework to study and quantify the impact of theoretical developments for GNNs. In the field of analyzing and learning from data on graphs, GNNs have become an essential tool. With promising applications in different domains such as chemistry, physics, social sciences, knowledge graphs, recommendation, and neuroscience, how to study and build more powerful GNNs is a hot topic. Without a standardized benchmark, it's hard even to define what constitutes a "powerful" GNN. In the paper Benchmarking Graph Neural Networks, researchers propose a flexible GNN benchmarking framework that can also accommodate the needs of researchers to add new datasets and models.