README.md

Open Graph Benchmark Lite (ogb_lite)

Open Graph Benchmark Lite (ogb_lite) is a subset of the ogb project. It supports library-agnostic loaders and it does not require torch.

99.99% of the code is copied from the OGB project:

• https://github.com/snap-stanford/ogb

We only make some small changes such that you can use ogb_lite without installing torch.

Installation

pip install ogb_lite

Tutorial

ogb_lite only contains three library-agnostic loaders: NodePropPredDataset, LinkPropPredDataset, and GraphPropPredDataset.

NodePropPredDataset:

# coding=utf-8
from ogb_lite.nodeproppred import NodePropPredDataset

dataset = NodePropPredDataset(name="ogbn-proteins")

split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"]
graph, label = dataset[0] # graph: library-agnostic graph object

print(graph, label)
print(train_idx, valid_idx, test_idx)

LinkPropPredDataset:

# coding=utf-8

from ogb_lite.linkproppred import LinkPropPredDataset

dataset = LinkPropPredDataset(name="ogbl-ppa")

split_edge = dataset.get_edge_split()
train_edge, valid_edge, test_edge = split_edge["train"], split_edge["valid"], split_edge["test"]
graph = dataset[0]  # graph: library-agnostic graph object

print(graph)
print(train_edge, valid_edge, test_edge)

GraphPropPredDataset:

# coding=utf-8

from ogb_lite.graphproppred import GraphPropPredDataset

dataset = GraphPropPredDataset(name="ogbg-molhiv")

split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"]

graph, label = dataset[0]  # graph: library-agnostic graph object
print(graph, label)
print(train_idx, valid_idx, test_idx)

Citing OGB

If you use OGB datasets in your work, please cite the OGB paper (Bibtex below).

@article{hu2020ogb,
  title={Open Graph Benchmark: Datasets for Machine Learning on Graphs},
  author={Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec},
  journal={arXiv preprint arXiv:2005.00687},
  year={2020}
}