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
2.9 KiB
Stochastic Training for Graph Convolutional Networks
- Paper: Control Variate
- Paper: Skip Connection
- Author's code: https://github.com/thu-ml/stochastic_gcn
Dependencies
- MXNet nightly build
pip install mxnet --pre
Neighbor Sampling & Skip Connection
cora: test accuracy ~83% with --num-neighbors 2, ~84% by training on the full graph
DGLBACKEND=mxnet python gcn_ns_sc.py --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
citeseer: test accuracy ~69% with --num-neighbors 2, ~70% by training on the full graph
DGLBACKEND=mxnet python gcn_ns_sc.py --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
pubmed: test accuracy ~76% with --num-neighbors 3, ~77% by training on the full graph
DGLBACKEND=mxnet python gcn_ns_sc.py --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
reddit: test accuracy ~91% with --num-neighbors 2 and --batch-size 1000, ~93% by training on the full graph
DGLBACKEND=mxnet python gcn_ns_sc.py --dataset reddit-self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 500 --n-hidden 64
Control Variate & Skip Connection
cora: test accuracy ~84% with --num-neighbors 1, ~84% by training on the full graph
DGLBACKEND=mxnet python gcn_cv_sc.py --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
citeseer: test accuracy ~69% with --num-neighbors 1, ~70% by training on the full graph
DGLBACKEND=mxnet python gcn_cv_sc.py --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
pubmed: test accuracy ~77% with --num-neighbors 1, ~77% by training on the full graph
DGLBACKEND=mxnet python gcn_cv_sc.py --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
reddit: test accuracy ~93% with --num-neighbors 1 and --batch-size 1000, ~93% by training on the full graph
DGLBACKEND=mxnet python gcn_cv_sc.py --dataset reddit-self-loop --num-neighbors 1 --batch-size 1000 --test-batch-size 500 --n-hidden 64
Control Variate & GraphSAGE-mean
Following Control Variate, we use the mean pooling architecture GraphSAGE-mean, two linear layers and layer normalization per graph convolution layer.
reddit: test accuracy 96.1% with --num-neighbors 1 and --batch-size 1000, ~96.2% in Control Variate with --num-neighbors 2 and --batch-size 1000
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