PyG-SSL: A Graph Self-Supervised Learning Toolkit

TL;DR

PyG-SSL is a comprehensive, user-friendly toolkit built on PyTorch that simplifies the implementation, evaluation, and reproduction of graph self-supervised learning algorithms for researchers and practitioners.

Contribution

It introduces a unified, accessible framework with tutorials and hyper-parameter settings, addressing reproducibility and usability challenges in graph SSL research.

Findings

Provides a comprehensive toolkit for graph SSL algorithms

Facilitates reproducibility with tutorials and hyper-parameters

Enhances accessibility for beginners and practitioners

Abstract

Graph Self-Supervised Learning (SSL) has emerged as a pivotal area of research in recent years. By engaging in pretext tasks to learn the intricate topological structures and properties of graphs using unlabeled data, these graph SSL models achieve enhanced performance, improved generalization, and heightened robustness. Despite the remarkable achievements of these graph SSL methods, their current implementation poses significant challenges for beginners and practitioners due to the complex nature of graph structures, inconsistent evaluation metrics, and concerns regarding reproducibility hinder further progress in this field. Recognizing the growing interest within the research community, there is an urgent need for a comprehensive, beginner-friendly, and accessible toolkit consisting of the most representative graph SSL algorithms. To address these challenges, we present a Graph SSL toolkit named PyG-SSL, which is built upon PyTorch and is compatible with various deep learning and scientific computing backends. Within the toolkit, we offer a unified framework encompassing dataset loading, hyper-parameter configuration, model training, and comprehensive performance evaluation for diverse downstream tasks. Moreover, we provide beginner-friendly tutorials and the best hyper-parameters of each graph SSL algorithm on different graph datasets, facilitating the reproduction of results. The GitHub repository of the library is https://github.com/iDEA-iSAIL-Lab-UIUC/pyg-ssl.