ogbg_ppa

ogbg-ppa

For a detailed description of the dataset, see the OGB website.

Models

• Graph Convolutional Networks (GCN) [1]
• Graph Isomorphism Networks (GIN) [2]

Dependencies

• OGB v1.2.1, which can be installed with pip install ogb

Usage

To run the script,

python main.py --gnn X

where X can be gcn, gin, gcn-virtual and gin-virtual. The postfix -virtual means that we will use a virtual node connected to all nodes in the graph for synchronizing information across all nodes.

By default, we use GPU whenever possible.

The optional arguments are as follows:

--dropout, dropout to use, (default=0.5)
--n_layers, number of GNN layers to use, (default=5)
--hidden_feats, number of hidden units in GNNs, (default=300)
--batch_size, batch size for training, (default=32)
--epochs, number of epochs for training, (default=100)
--num_workers, number of processes for data loading, (default=1)
--filename, filename to output results. By default, it will be the same as the gnn used.

Performance

Using the default parameters, the performance of 10 random runs is as follows.

Method	Accuracy (%)
GCN	67.80 +- 0.49
GIN	69.31 +- 1.94
GCN-virtual	69.02 +- 0.47
GIN-virtual	70.62 +- 0.70

References

[1] Kipf T., Welling M. Semi-Supervised Classification with Graph Convolutional Networks. 2016.

[2] Xu K., Hu W., Leskovec J., Jegelka S. How Powerful are Graph Neural Networks? 2019.