GammaGL/examples/grace_pot

• GammaGL Implementation of GRACE-POT
• • Example Implementor
  • Datasets
  • • • • Unsupervised Node Classification Datasets:
  • How to run examples
  • Performance

GammaGL Implementation of GRACE-POT

This GammaGL example implements the model proposed in the paper xxxx.

Author's code:

Example Implementor

This example was implemented by Siyuan Zhang

Datasets

Unsupervised Node Classification Datasets:

'Cora', 'Citeseer' and 'Pubmed'

Dataset	# Nodes	# Edges	# Classes
Cora	2,708	10,556	7
Pubmed	19,717	88,651	3
Photo	7,650	238,162	8

How to run examples

Fisrt, make the directories for datasets and bounds to save

mkdir ~/datasets
mkdir ~/datasets/bounds

Then, go into the directory of a model. If you want to set the parameters, you should modify the ocnfiguration files in the directory ("config.yaml" for GRACE). The following is the command line to run each model (dataset used is Cora for example):

# original GRACE
python GRACE_POT_trainer.py --dataset Cora --gpu_id 0
# GRACE + POT
python GRACE_POT_trainer.py --dataset Cora --gpu_id 0 --use_pot --kappa 0.4

The result will be appended to the file "res/{dataset_name}_base_temp.csv" and "res/{dataset_name}_pot_temp.csv" respectively. You can also set the parameter "save_file" to specify the file to save results. We use minibatch to reduce the memory occupation, you can modify it in the code.

Performance

                    |   Author's Code   |  GAMMAGL's Code   |
| Dataset | Metrics |  GRACE  |GRACE-POT|  GRACE  |GRACE-POT|
|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|
|  Cora   |  Mi-F1  |   78.2  |   79.2  |   78.2  |   82.2  |
|  Cora   |  Ma-F1  |   76.8  |   77.8  |   77.1  |   81.3  |
|  PubMed |  Mi-F1  |   81.6  |   82.0  |   81.6  |   82.0  |
|  PubMed |  Ma-F1  |   81.7  |   82.4  |   80.5  |   80.1  |
|  Photo  |  Mi-F1  |   91.2  |   91.8  |   89.8  |   90.0  |
|  Photo  |  Ma-F1  |   89.2  |   90.0  |   88.5  |   87.9  |