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SupplyGraph is a benchmark dataset for supply chain planning, especially suited for applications of Graph Neural Networks (GNNs). The dataset originates from a leading Fast-Moving Consumer Goods (FMCG) company in Bangladesh and includes time‑series data as node features for sales forecasting, production planning, and factory problem identification. Researchers can apply GNNs to a variety of supply‑chain problems, advancing analysis and planning in the field.

`ogbg‑molhiv` is a small molecular property prediction dataset adapted from MoleculeNet by the Stanford team for the Open Graph Benchmark. It is a binary classification task predicting whether a molecule inhibits HIV, evaluated with ROC‑AUC. The dataset comprises 41,127 graphs, each with node features, edge indices, edge attributes, and labels, following the PyGeometric split.