dmlc--dgl
Minjie Wang
708765f0a1
* rgcn module * support id input * WIP: model codes * use faster index select * dropout * self loop * WIP: link prediction * fix lint * WIP: docs * docstring * docstring * merge two child classes * mxnet rgcn module * fix lint * fix lint * fix rename bug * add uniform edge sampler * fix fn name * docstring * fix mxnet rgcn module * fix mx rgcn * enable test on cuda
37 行
1.1 KiB
Markdown
37 行
1.1 KiB
Markdown
# Relational-GCN
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* Paper: [https://arxiv.org/abs/1703.06103](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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Two extra python packages are needed for this example:
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- MXNet nightly build
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- requests
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- rdflib
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- pandas
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```bash
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pip install mxnet --pre
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pip install requests rdflib pandas
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```
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Example code was tested with rdflib 4.2.2 and pandas 0.23.4
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### Entity Classification
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AIFB: accuracy 97.22% (DGL), 95.83% (paper)
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```
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DGLBACKEND=mxnet python3 entity_classify.py -d aifb --testing --gpu 0
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```
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MUTAG: accuracy 73.53% (DGL), 73.23% (paper)
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```
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DGLBACKEND=mxnet python3 entity_classify.py -d mutag --l2norm 5e-4 --n-bases 40 --testing --gpu 0
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```
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BGS: accuracy 75.86% (DGL, n-basese=20, OOM when >20), 83.10% (paper)
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```
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DGLBACKEND=mxnet python3 entity_classify.py -d bgs --l2norm 5e-4 --n-bases 20 --testing --gpu 0 --relabel
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```
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