Relational-GCN

Paper: Modeling Relational Data with Graph Convolutional Networks
Author's code for entity classification: https://github.com/tkipf/relational-gcn
Author's code for link prediction: https://github.com/MichSchli/RelationPrediction

Dependencies

rdflib
torchmetrics 0.11.4

Install as follows:

pip install rdflib
pip install torchmetrics==0.11.4

How to run

Entity Classification

Run with the following for entity classification (available datasets: aifb (default), mutag, bgs, and am)

python3 entity.py --dataset aifb

For mini-batch training, run with the following (available datasets are the same as above)

python3 entity_sample.py --dataset aifb

For multi-gpu training (with sampling), run with the following (same datasets and GPU IDs separated by comma)

python3 entity_sample_multi_gpu.py --dataset aifb --gpu 0,1

Link Prediction

Run with the following for link prediction on dataset FB15k-237 with filtered-MRR

python link.py

NOTE: By default, we use uniform edge sampling instead of neighbor-based edge sampling as in author's code. In practice, we find that it can achieve similar MRR.

Summary

Entity Classification

Dataset	Full-graph	Mini-batch
aifb	~0.85	~0.82
mutag	~0.70	~0.50
bgs	~0.86	~0.64
am	~0.78	~0.42

Link Prediction

Dataset	Best MRR
FB15k-237	~0.2397