Open Graph Benchmark

The Open Graph Benchmark (OGB) is a collection of large-scale graph datasets designed to facilitate research and development in graph machine learning. Current research focuses on improving the efficiency and scalability of graph neural networks (GNNs) on these datasets, exploring architectures like message-passing neural networks (MPNNs), transformers, and hybrid models, as well as optimization techniques such as graph pruning and efficient training strategies. OGB's standardized datasets and evaluation metrics are driving advancements in various applications, including molecular property prediction, article classification, and link prediction, ultimately advancing the field of graph representation learning.