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Hand-curated, famous, and ready graph datasets.

Project description

Graph Sets

Hand-curated graph datasets for fast experimentation with graph neural networks. Loaders return PyTorch-ready tensors so you can focus on modeling instead of wrangling benchmark data.

Installation

  • pip install graphsets

Usage

from graphsets import load_cora

# directory containing cora.content and cora.cites from the standard Cora release
data_dir = "/path/to/cora"

features, adj, labels, idx_train, idx_val, idx_test = load_cora(data_dir)

What you get

  • Features: float tensor of node attributes (row-normalized).
  • Adjacency: symmetric 0/1 tensor built from cora.cites.
  • Labels: integer class ids matching the original paper labels.
  • Splits: simple train/val/test indices (140/300/1000 nodes) for quick baselines.

Datasets

  • cora — citation network commonly used for GCN/GAT benchmarks.

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