Metadata-Version: 2.4
Name: graphsets
Version: 0.0.12
Summary: Hand-curated, famous, and ready graph datasets.
Author-email: Taha Shieenavaz <tahashieenavaz@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Taha Shieenavaz
        
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Project-URL: Homepage, https://github.com/tahashieenavaz/graphsets
Project-URL: Repository, https://github.com/tahashieenavaz/graphsets
Project-URL: Documentation, https://github.com/tahashieenavaz/graphsets#readme
Keywords: graph,graph datasets,graph attention networks,gat,graph deep learning
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: torch
Dynamic: license-file

# 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

```python
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.
