Citeseer )¶

This page tries to reproduce all the Graph Neural Network paper for Citation Network (Cora/Pubmed/Citeseer) with the public train/dev/test split, which is the Hello world dataset (small and fast) for graph neural networks. But it’s very hard to achieve very high performance.

All datasets are runned with public split of semi-supervised settings. And we report the averarge accuracy by running 10 times.

Experiment Results¶

Model	Cora	Pubmed	Citeseer	Remarks
Vanilla GCN (Kipf 2017)	0.807(0.010)	0.794(0.003)	0.710(0.007)
GAT (Veličković 2017)	0.834(0.004)	0.772(0.004)	0.700(0.006)
SGC(Wu 2019)	0.818(0.000)	0.782(0.000)	0.708(0.000)
APPNP (Johannes 2018)	0.846(0.003)	0.803(0.002)	0.719(0.003)	Almost the same with the results reported in Appendix E.
GCNII (64 Layers, 1500 Epochs, Chen 2020)	0.846(0.003)	0.798(0.003)	0.724(0.006)
SSGC (Zhu 2021)	0.834(0.000)	0.796(0.000)	0.734(0.000)	Weight decay is important, 1e-4 for Citeseer/ 5e-6 for Cora / 5e-6 for Pubmed

How to run the experiments?¶

# Device choose
# use GPU
export CUDA_VISIBLE_DEVICES=0
# use CPU
export CUDA_VISIBLE_DEVICES=

# Experimental API
# If you want to try MultiGPU-FullBatch training. Run the following code instead.
# This will only speed up models that have more computation on edges.
# For example, the TransformerConv in [Yun 2020](https://arxiv.org/abs/2009.03509).

CUDA_VISIBLE_DEVICES=0,1 multi_gpu_train.py --conf config/transformer.yaml

# GCN
python train.py --conf config/gcn.yaml  --dataset cora
python train.py --conf config/gcn.yaml  --dataset pubmed
python train.py --conf config/gcn.yaml  --dataset citeseer

# GAT
python train.py --conf config/gat.yaml --dataset cora
python train.py --conf config/gat.yaml --dataset pubmed
python train.py --conf config/gat.yaml --dataset citeseer

# SGC
python train.py --conf config/sgc.yaml --dataset cora
python train.py --conf config/sgc.yaml --dataset pubmed
python train.py --conf config/sgc.yaml --dataset citeseer

# APPNP
python train.py --conf config/appnp.yaml --dataset cora
python train.py --conf config/appnp.yaml --dataset pubmed
python train.py --conf config/appnp.yaml --dataset citeseer

# GCNII (The original code use 1500 epochs.)
python train.py --conf config/gcnii.yaml --dataset cora --epoch 1500
python train.py --conf config/gcnii.yaml --dataset pubmed --epoch 1500
python train.py --conf config/gcnii.yaml --dataset citeseer --epoch 1500

# TransformConv + Gated Residual
python train.py --conf config/transformer.yaml --dataset cora
python train.py --conf config/transformer.yaml --dataset pubmed
python train.py --conf config/transformer.yaml --dataset citeseer

# SSGC
python train.py --conf config/sgc.yaml --dataset cora
python train.py --conf config/sgc.yaml --dataset pubmed
python train.py --conf config/sgc.yaml --dataset citeseer