Gophormer: Ego-Graph Transformer for Node Classification

This repository is an implementation of Gophormer - Gophormer: Ego-Graph Transformer for Node Classification. The work is completed during Jianan's internship at Microsoft Research Asia.

Requirements

We use the miniconda to manage the python environment. We provide the environment.yaml to implement the environment

conda env create -f environment.yml

Running

Flickr

python src/Gophormer/models/Gophormer/train.py --batch_size=32 --cr_temperature=0.3 --cr_weight=1 --dataset=flickr --dropout=0.1 --early_stop=30 --epochs=200 --eval_freq=3 --global_nodes=4 --gt_n_layers=5 --lr=0.0002 --n_samples=3 --n_workers=1 --norm=PreLN --proximity_encoding=1i --readout=MLP_1_center --readout_dropout=0 --sample=EgoGraph-5_1 --sample_dev=cpu --scheduler=Cos5_0.2_0.5_1 --trainer=node --val_inference=SubGVote-5 --wandb_name=FlickrRough --gpus=7

Blog

python src/Gophormer/models/Gophormer/train.py --batch_size=32 --cr_temperature=0.8 --cr_weight=0.8 --dataset=blogcatalog --dropout=0.1 --early_stop=30 --epochs=200 --eval_freq=3 --global_nodes=5 --gt_n_layers=4 --lr=0.0001 --n_samples=4 --n_workers=1 --norm=PreLN --proximity_encoding=1i --readout=MLP_1_center --readout_dropout=0 --sample=EgoGraph-7_1 --sample_dev=cpu --scheduler=Cos5_0.2_0.25_1.25 --trainer=node --val_inference=SubGVote-5 --wandb_name=FinalSearch-Blog --gpus=0

Pubmed

python src/Gophormer/models/Gophormer/train.py --batch_size=32 --cr_weight=0.5 --dataset=pubmed --dropout=0.3 --early_stop=30 --epochs=200 --eval_freq=3 --global_nodes=1 --gt_n_layers=4 --lr=0.00015 --n_samples=2 --n_workers=1 --norm=PreLN --proximity_encoding=1i --readout=MLP_1_center --readout_dropout=0 --sample=EgoGraph-5_2 --sample_dev=cpu --scheduler=Cos5_0.2_0.5_1.25 --trainer=node --val_inference=SubGVote-5 --wandb_name=PubmedRough_0.2 --gpus=1

DBLP

python src/Gophormer/models/Gophormer/train.py --batch_size=32 --cr_temperature=0.5 --cr_weight=0.5 --dataset=dblp --dropout=0.1 --early_stop=30 --epochs=200 --eval_freq=3 --global_nodes=3 --gt_n_layers=4 --lr=0.0001 --n_samples=2 --n_workers=1 --norm=PreLN --proximity_encoding=2i --readout=MLP_1_center --readout_dropout=0 --sample=EgoGraph-5_2 --sample_dev=cpu --scheduler=Cos5_0.2_0.25_1.25 --trainer=node --val_inference=SubGVote-5 --wandb_name=DBLP_Rough_Bayes --gpus=0

Citeseer

python src/Gophormer/models/Gophormer/train.py --batch_size=32 --cr_temperature=0.4 --cr_weight=0.3 --dataset=citeseer --dropout=0.4 --early_stop=30 --epochs=200 --eval_freq=3 --global_nodes=2 --gt_n_layers=2 --lr=0.0002 --n_samples=4 --n_workers=1 --norm=PreLN --proximity_encoding=1i --readout=MLP_1_center --readout_dropout=0 --sample=EgoGraph-7_2 --sample_dev=cpu --scheduler=Cos5_0.2_0.25_1.25 --trainer=node --val_inference=SubGVote-5 --wandb_name=FinalSearch-Citeseer --gpus=3

Cora

python src/Gophormer/models/Gophormer/train.py --batch_size=32 --cr_weight=0.5 --dataset=cora --dropout=0.30000000000000004 --early_stop=30 --epochs=200 --eval_freq=3 --global_nodes=4 --gt_n_layers=2 --lr=0.00015000000000000001 --n_samples=5 --n_workers=1 --norm=PreLN --proximity_encoding=1i --readout=MLP_1_center --readout_dropout=0 --sample=EgoGraph-7_2 --sample_dev=cpu --scheduler=Cos5_0.2_0.5_1 --trainer=node --val_inference=SubGVote-5 --wandb_name=coraRough_0.3 --gpus=1

Citation

If you find our work useful, please consider citing our work:

@article{DBLP:journals/corr/abs-2110-13094,
  author    = {Jianan Zhao and
               Chaozhuo Li and
               Qianlong Wen and
               Yiqi Wang and
               Yuming Liu and
               Hao Sun and
               Yanfang Ye and
               Xing Xie},
  title     = {Gophormer: Ego-Graph Transformer for Node Classification},
  journal   = {CoRR},
  volume    = {abs/2110.13094},
  year      = {2021},
  url       = {https://arxiv.org/abs/2110.13094},
  eprinttype = {arXiv},
  eprint    = {2110.13094},
  timestamp = {Tue, 18 Oct 2022 14:44:48 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2110-13094.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}