RGCN: Modeling Relational Data with Graph Convolutional Networks

RGCN is a graph convolutional networks applied in heterogeneous graph.

Its message-passing equation is as follows:

:math:`` h{i}^{(l+1)}=sigmaleft(sum{r in mathcal{R}} sum{j in mathcal{N}{r}(i)} W{r}^{(l)} h{j}^{(l)}right) $$

From the equation above, we can see that there are two parts in the computation.

1, Message aggregation within each relation \(r\) (edge_type).

2, Reduction that merges the results from multiple relationships.

Datasets

Here, we use MUTAG dataset to reproduce this model. The dataset can be downloaded from here.

Dependencies

• paddlepaddle>=2.0

• pgl>=2.1

How to run

To train a RGCN model on MUTAG dataset, you can just run

export CUDA_VISIBLE_DEVICES=0

python train.py --data_path /your/path/to/mutag_data

If you want to train a RGCN model with multiple GPUs, you can just run with fleetrun API with CUDA_VISIBLE_DEVICES

CUDA_VISIBLE_DEVICES=0,1 fleetrun train.py --data_path /your/path/to/mutag_data

Hyperparameters

• data_path: The directory of your dataset.

• epochs: Number of epochs default (10)

• input_size: Input dimension.

• hidden_size: The hidden size for the RGCN model.

• num_class: The number of classes to be predicted.

• num_layers: The number of RGCN layers to be applied.

• num_bases: Number of basis decomposition

• seed: Random seed.

• lr: Learning rate.

Performance

We train the RGCN model for 10 epochs and report the besst accuracy on the test dataset.

Dataset	Accuracy	Reported in paper
MUTAG	77.94%	73.23%