  Graph neural networks (GNNs) have shown high potential for a variety of
real-world, challenging applications, but one of the major obstacles in GNN
research is the lack of large-scale flexible datasets. Most existing public
datasets for GNNs are relatively small, which limits the ability of GNNs to
generalize to unseen data. The few existing large-scale graph datasets provide
very limited labeled data. This makes it difficult to determine if the GNN
model's low accuracy for unseen data is inherently due to insufficient training
data or if the model failed to generalize. Additionally, datasets used to train
GNNs need to offer flexibility to enable a thorough study of the impact of
various factors while training GNN models.
  In this work, we introduce the Illinois Graph Benchmark (IGB), a research
dataset tool that the developers can use to train, scrutinize and
systematically evaluate GNN models with high fidelity. IGB includes both
homogeneous and heterogeneous academic graphs of enormous sizes, with more than
40% of their nodes labeled. Compared to the largest graph datasets publicly
available, the IGB provides over 162X more labeled data for deep learning
practitioners and developers to create and evaluate models with higher
accuracy. The IGB dataset is a collection of academic graphs designed to be
flexible, enabling the study of various GNN architectures, embedding generation
techniques, and analyzing system performance issues for node classification
tasks. IGB is open-sourced, supports DGL and PyG frameworks, and comes with
releases of the raw text that we believe foster emerging language models and
GNN research projects. An early public version of IGB is available at
https://github.com/IllinoisGraphBenchmark/IGB-Datasets.
