wehub-resource-sync
62 lines
1.7 KiB
Markdown
62 lines
1.7 KiB
Markdown
# Relational-GCN
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* Paper: [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103)
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* Author's code for entity classification: [https://github.com/tkipf/relational-gcn](https://github.com/tkipf/relational-gcn)
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* Author's code for link prediction: [https://github.com/MichSchli/RelationPrediction](https://github.com/MichSchli/RelationPrediction)
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### Dependencies
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- rdflib
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- torchmetrics 0.11.4
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Install as follows:
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```bash
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pip install rdflib
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pip install torchmetrics==0.11.4
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```
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How to run
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-------
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### Entity Classification
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Run with the following for entity classification (available datasets: aifb (default), mutag, bgs, and am)
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```bash
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python3 entity.py --dataset aifb
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```
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For mini-batch training, run with the following (available datasets are the same as above)
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```bash
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python3 entity_sample.py --dataset aifb
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```
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For multi-gpu training (with sampling), run with the following (same datasets and GPU IDs separated by comma)
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```bash
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python3 entity_sample_multi_gpu.py --dataset aifb --gpu 0,1
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```
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### Link Prediction
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Run with the following for link prediction on dataset FB15k-237 with filtered-MRR
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```bash
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python link.py
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```
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> **_NOTE:_** By default, we use uniform edge sampling instead of neighbor-based edge sampling as in [author's code](https://github.com/MichSchli/RelationPrediction). In practice, we find that it can achieve similar MRR.
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Summary
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-------
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### Entity Classification
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| Dataset | Full-graph | Mini-batch
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| ------------- | ------- | ------
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| aifb | ~0.85 | ~0.82
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| mutag | ~0.70 | ~0.50
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| bgs | ~0.86 | ~0.64
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| am | ~0.78 | ~0.42
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### Link Prediction
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| Dataset | Best MRR
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| ------------- | -------
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| FB15k-237 | ~0.2397
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