PyTorch Geometric code for the paper "R-GCN: The R Could Stand for Random"
RR-GCN is an extension of Relational Graph Convolutional Networks (R-GCN) in which the weights are randomly initialised and kept frozen (i.e. no training step is required). As such, our technique is unsupervised and the produced embeddings can be used for multiple downstream ML tasks. Surprisingly, empirical results indicate that the embeddings produced by our RR-GCN can be competitive, and even sometimes outperform, the original R-GCN.
This snippet generates train and test node embeddings for a KG stored as a PyG Data object.
from rrgcn import RRGCNEmbedder
embedder = RRGCNEmbedder(num_nodes=data.num_nodes,
num_relations=dataset.num_relations,
num_layers=2,
emb_size=2048,
ppv=True)
embeddings = embedder.embeddings(data.edge_index,
data.edge_type,
batch_size=0, # full-batch
idx=torch.hstack((data.train_idx, data.test_idx)))
train_embeddings = embeddings[:len(data.train_idx)]
test_embeddings = embeddings[len(data.train_idx):] Documentation for RRGCNEmbedder
Documentation for embeddings()
Example notebook for the AIFB dataset.
After installing the correct PyG version for your PyTorch/CUDA installation as documented in their installation instructions, install the RR-GCN package using:
pip install git+https://github.com/predict-idlab/RR-GCN
@article{degraeve2022rgcn,
author = {Degraeve, Vic and Vandewiele, Gilles and Ongenae, Femke and Van Hoecke, Sofie},
title = {R-GCN: The R Could Stand for Random},
journal = {arXiv},
year = {2022},
url = {https://arxiv.org/abs/2203.02424}
}