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Graph neural networks (GNNs) have shown high potential for a variety ofreal-world, challenging applications, but one of the major obstacles in GNNresearch is the lack of large-scale flexible datasets. Most existing publicdatasets for GNNs are relatively small, which limits the ability of GNNs togeneralize to unseen data. The few existing large-scale graph datasets providevery limited labeled data. This makes it difficult to determine if the GNNmodel's low accuracy for unseen data is inherently due to insufficient trainingdata or if the model failed to generalize. Additionally, datasets used to trainGNNs need to offer flexibility to enable a thorough study of the impact ofvarious factors while training GNN models.  In this work, we introduce the Illinois Graph Benchmark (IGB), a researchdataset tool that the developers can use to train, scrutinize andsystematically evaluate GNN models with high fidelity. IGB includes bothhomogeneous and heterogeneous academic graphs of enormous sizes, with more than40% of their nodes labeled. Compared to the largest graph datasets publiclyavailable, the IGB provides over 162X more labeled data for deep learningpractitioners and developers to create and evaluate models with higheraccuracy. The IGB dataset is a collection of academic graphs designed to beflexible, enabling the study of various GNN architectures, embedding generationtechniques, and analyzing system performance issues for node classificationtasks. IGB is open-sourced, supports DGL and PyG frameworks, and comes withreleases of the raw text that we believe foster emerging language models andGNN research projects. An early public version of IGB is available athttps://github.com/IllinoisGraphBenchmark/IGB-Datasets.

IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research