dmlc--dgl
Ziyue Huang
7e30382e4f
* neighbor sampling draft * val/test acc * control variate draft * control variate * update * fix new_history * maintain aggregated history while updating new history * preprocess the first layer, change push to pull * update * fix subg_degree * nodeflow * clear * readme * doc and unittest for self loop * address comments * rename * update * fix * Update node_flow.py * Update node_flow.py
67 行
2.9 KiB
Markdown
67 行
2.9 KiB
Markdown
# Stochastic Training for Graph Convolutional Networks
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* Paper: [Control Variate](https://arxiv.org/abs/1710.10568)
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* Paper: [Skip Connection](https://arxiv.org/abs/1809.05343)
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* Author's code: [https://github.com/thu-ml/stochastic_gcn](https://github.com/thu-ml/stochastic_gcn)
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### Dependencies
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- MXNet nightly build
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```bash
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pip install mxnet --pre
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```
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### Neighbor Sampling & Skip Connection
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cora: test accuracy ~83% with `--num-neighbors 2`, ~84% by training on the full graph
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```
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DGLBACKEND=mxnet python gcn_ns_sc.py --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
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```
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citeseer: test accuracy ~69% with `--num-neighbors 2`, ~70% by training on the full graph
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```
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DGLBACKEND=mxnet python gcn_ns_sc.py --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
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```
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pubmed: test accuracy ~76% with `--num-neighbors 3`, ~77% by training on the full graph
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```
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DGLBACKEND=mxnet python gcn_ns_sc.py --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
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```
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reddit: test accuracy ~91% with `--num-neighbors 2` and `--batch-size 1000`, ~93% by training on the full graph
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```
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DGLBACKEND=mxnet python gcn_ns_sc.py --dataset reddit-self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 500 --n-hidden 64
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```
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### Control Variate & Skip Connection
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cora: test accuracy ~84% with `--num-neighbors 1`, ~84% by training on the full graph
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```
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DGLBACKEND=mxnet python gcn_cv_sc.py --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
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```
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citeseer: test accuracy ~69% with `--num-neighbors 1`, ~70% by training on the full graph
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```
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DGLBACKEND=mxnet python gcn_cv_sc.py --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
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```
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pubmed: test accuracy ~77% with `--num-neighbors 1`, ~77% by training on the full graph
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```
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DGLBACKEND=mxnet python gcn_cv_sc.py --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
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```
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reddit: test accuracy ~93% with `--num-neighbors 1` and `--batch-size 1000`, ~93% by training on the full graph
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```
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DGLBACKEND=mxnet python gcn_cv_sc.py --dataset reddit-self-loop --num-neighbors 1 --batch-size 1000 --test-batch-size 500 --n-hidden 64
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```
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### Control Variate & GraphSAGE-mean
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Following [Control Variate](https://arxiv.org/abs/1710.10568), we use the mean pooling architecture GraphSAGE-mean, two linear layers and layer normalization per graph convolution layer.
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reddit: test accuracy 96.1% with `--num-neighbors 1` and `--batch-size 1000`, ~96.2% in [Control Variate](https://arxiv.org/abs/1710.10568) with `--num-neighbors 2` and `--batch-size 1000`
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```
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DGLBACKEND=mxnet python graphsage_cv.py --batch-size 1000 --test-batch-size 500 --n-epochs 50 --dataset reddit --num-neighbors 1 --n-hidden 128 --dropout 0.2 --weight-decay 0
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```
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