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README.md
gcn_cv_sc.py
gcn_ns_sc.py

README.md

Stochastic Training for Graph Convolutional Networks

Dependencies

  • PyTorch 0.4.1+
  • requests

bash pip install torch requests

Neighbor Sampling & Skip Connection

cora: test accuracy ~83% with --num-neighbors 2, ~84% by training on the full graph

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

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

python gcn_ns_sc.py --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --test-batch-size 1000000 --gpu 0

Control Variate & Skip Connection

cora: test accuracy ~84% with --num-neighbors 1, ~84% by training on the full graph

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

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

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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