/examples/pytorch/sampling

Stochastic Training for Graph Convolutional Networks

• Paper: Control Variate
• Paper: Skip Connection
• Author's code: https://github.com/thu-ml/stochastic_gcn

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

DGLBACKEND=pytorch python3 gcn_ns_sc.py --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000

citeseer

Test accuracy ~69% with --num-neighbors 2, ~70% by training on the full graph

DGLBACKEND=pytorch python3 gcn_ns_sc.py --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000

pubmed

Test accuracy ~76% with --num-neighbors 3, ~77% by training on the full graph

DGLBACKEND=pytorch python3 gcn_ns_sc.py --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --test-batch-size 1000000

Control Variate & Skip Connection

cora

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

DGLBACKEND=pytorch python3 gcn_cv_sc.py --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000

citeseer

Test accuracy ~69% with --num-neighbors 1, ~70% by training on the full graph

DGLBACKEND=pytorch python3 gcn_cv_sc.py --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000

pubmed

Test accuracy ~77% with --num-neighbors 1, ~77% by training on the full graph

DGLBACKEND=pytorch python3 gcn_cv_sc.py --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000