[CIKM 2022]-Towards Self-supervised Learning on Graphs with Heterophily

Source code of HGRL model proposed in the CIKM 2022 paper Towards Self-supervised Learning on Graphs with Heterophily.

Dependencies

• python 3.9.13
• pytorch 1.12.0
• pytorch-geometric 2.0.4
• scikit-learn 1.1.1
• ogb 1.3.3
• numpy 1.23.1
• munkres 1.1.4
• googledrivedownloader 0.4
• networkx 2.8.5
• matplotlib 3.5.2

Datasets

Heterogeneous datasets: 'Cornell', Texas', 'Wisconsin', 'Actor', 'Squirrel' and 'Chameleon'.

Homogeneous dataset: 'Cora', 'CiteSeer' and 'PubMed'.

Dataset	# Nodes	# Edges	# Classes	# Features	# Homo. ratio
Texas	183	295	5	1,703	0.11
Wisconsin	251	466	5	1,703	0.21
Actor	7,600	26,752	5	931	0.22
Squirrel	5,201	198,493	5	2,089	0.22
Chameleon	2,277	31,421	5	2,325	0.23
Cornell	183	280	5	1,703	0.3
CiteSeer	3,327	4,676	7	3,703	0.74
PubMed	19,717	44,327	3	500	0.8
Cora	2,708	5,278	6	1,433	0.81

Usage

To run the codes, use the following commands:

# Cora
python main.py --dataset Cora --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.4 --method hn2n_CCA_init_adj --task node_classification --topology_augmentation init

# CiteSeer
python main.py --dataset CiteSeer --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.4 --method hn2n_CCA_init_adj --task node_classification --topology_augmentation init

# PubMed
python main.py --dataset PubMed --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.4 --method hn2n_CCA_init_adj --task node_classification --topology_augmentation init

# Texas
python main.py --dataset texas --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.4 --method hn2n_CCA_learned_adj --task node_classification

# Wisconsin
python main.py --dataset wisconsin --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.4 --method hn2n_CCA_learned_adj --task node_classification

# Actor
python main.py --dataset film --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.4 --method hn2n_CCA_learned_adj --task node_classification

# Squirrel 
python main.py --dataset squirrel --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.4 --method hn2n_CCA_init_adj --task node_classification --topology_augmentation init

# Chameleon
python main.py --dataset chameleon --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.3 --method hn2n_CCA_init_adj --task node_classification --topology_augmentation init

# Cornell
python main.py --dataset cornell --epochs 500 --lr 0.005 --lr_gamma 0.005 --weight_decay 0.0005 --hidden_size 512 --output_size 512 --dropout 0.4 --method hn2n_CCA_learned_adj --task node_classification

For node cluster task, please use "--task node_cluster" and "--output_size 16".