Title: Open Graph Benchmark Large-Scale Challenge
We first present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are larger than existing graph benchmarks, encompass multiple important graph ML tasks, and cover a diverse range of domains. We then present OGB’s new initiative on a Large-Scale Challenge (OGB-LSC) at the KDD Cup 2021. OGB-LSC provides datasets that represent modern industrial-scale large graphs. We provide dedicated baseline experiments, scaling up expressive graph ML models to the massive datasets. We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale.
Weihua Hu is a Ph.D. student of Computer Science at Stanford University, advised by Jure Leskovec. His research interests lie in graph representation learning and its applications to scientific discovery. His recent research is on advancing the field of Graph Neural Networks, by improving their theoretical understanding and generalization capability as well as building large-scale datasets for benchmarking models. He also actively applies his research to drug discovery and material discovery. He is supported by Funai Overseas Scholarship and Masason Foundation Fellowship. Before joining Stanford, Weihua received his Bachelor’s and Master’s degrees both from the University of Tokyo, where he received the best Master’s thesis award.