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 <https://github.com/snap-stanford/ogb>_

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

Installation

.. code-block:: bash

pip install ogb_lite

Tutorial

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

NodePropPredDataset:

.. code-block:: python

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:

.. code-block:: python

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:

.. code-block:: python

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).

.. code-block:: HTML

@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} }