GammaGL Implementation of MERIT
This GammaGL example implements the model proposed in the paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning.
Author's code: https://github.com/GRAND-Lab/MERIT
Example Implementor
This example was implemented by Ziyu Zheng
Datasets
Unsupervised Node Classification Datasets:
'Cora', 'Citeseer' and 'Pubmed'
Dataset |
# Nodes |
# Edges |
# Classes |
Cora |
2,708 |
10,556 |
7 |
Citeseer |
3,327 |
9,228 |
6 |
Pubmed |
19,717 |
88,651 |
3 |
Arguments
--input_dim int Input dimension. Default is 1433.
--out_dim int Output dimension. Default is 512.
--proj_size int Encoder output dimension Default is 512.
--proj_hid int Encoder hidden dimension Default is 4096.
--pred_size int MLP output dimension Default is 512.
--pred_hid int MLP hidden dimension Default is 4096.
--drop_edge_rate_1 float Drop edge ratio 1. Default is 0.2.
--drop_edge_rate_2 float Drop edge ratio 2. Default is 0.2.
--drop_feature_rate_1 float Drop feature ratio 1. Default is 0.5.
--drop_feature_rate_2 float Drop feature ratio 2. Default is 0.5.
--dataset_path str path to save dataset. Default is r'../'
How to run examples
In the paper(as well as authors' repo), the training set are full graph training
# use paddle backend
# Cora by GammaGL
TL_BACKEND=paddle python merit_trainer.py --dataset cora --epochs 500 --drop_edge_rate_1 0.2 --drop_edge_rate_2 0.2 --drop_feature_rate_1 0.5 --drop_feature_rate_2 0.5 --lr 3e-4 --beta 0.5
#Citeseer by GammaGL
TL_BACKEND=paddle python merit_trainer.py --dataset citeseer --epochs 500 --drop_edge_rate_1 0.4 --drop_edge_rate_2 0.4 --drop_feature_rate_1 0.5 --drop_feature_rate_2 0.5 --lr 3e-4 --beta 0.6
# use tensorflow backend
# Cora by GammaGL
TL_BACKEND=tensorflow python merit_trainer.py --dataset cora --epochs 500 --drop_edge_rate_1 0.2 --drop_edge_rate_2 0.2 --drop_feature_rate_1 0.5 --drop_feature_rate_2 0.5 --lr 3e-4 --beta 0.5
#Citeseer by GammaGL
TL_BACKEND=tensorflow python merit_trainer.py --dataset citeseer --epochs 500 --drop_edge_rate_1 0.4 --drop_edge_rate_2 0.4 --drop_feature_rate_1 0.5 --drop_feature_rate_2 0.5 --lr 3e-4 --beta 0.6
Performance
Dataset |
Cora |
Citeseer |
Pubmed |
Author's Code |
83.1 |
74.0 |
80.2 |
GammaGL(tf) |
84.3 |
72.2 |
--.- |
GammaGL(paddle) |
83.1 |
--.- |
--.- |