opengsl 0.0.4

A comprehensive benchmark for Graph Structure Learning.

OpenGSL

OpenGSL is a benchmark for Graph Structure Learning.

Overview of the benchmark

OpenGSL provides a fair and comprehensive platform to evaluate existing GSL works and facilitate future GSL researches.

Installation

Using Pip

pip install opengsl

Installation for local development:

git clone https://github.com/OpenGSL/OpenGSL
cd opengsl
pip install -e .

Quick Start

Following example shows you how to perform GCN on Cora dataset.

Step 1: Load configuration

import opengsl
conf = opengsl.load_conf(method="gcn", dataset="cora")

Step 2: Load data

dataset = opengsl.data.Dataset("cora", n_splits=1, feat_norm=conf.dataset['feat_norm'])

Step 3: Build Model

solver = opengsl.method.gcn(conf,dataset)

Step 4: Training and Evaluation

exp = opengsl.ExpManager(solver, n_runs = 10)
exp.run()

Add method

if you want to use your own method, see example.py for detail.

How to Contribute

As an active research topic, we are witenessing the rapid development of GSL methods. Hence, this project will be frequently updated and we welcome everyone interested in this topic to contribute!

Please feel free to send PR or issue!

Citation

Our paper on this benchmark will be released soon!

If you use our benchmark in your works, we would appreciate citations to the paper:

@article{zhou2023opengsl,
  title={OpenGSL: A Comprehensive Benchmark for Graph Structure Learning},
  author={Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Can Wang, Yan Feng, Chun Chen},
  journal={arXiv preprint},
  year={2023}
}