Authors: Lecheng Zheng, Baoyu Jing, Zihao Li, Zhichen Zeng, Tianxin Wei, Mengting Ai, Xinrui He, Lihui Liu, Dongqi Fu, Jiaxuan You, Hanghang Tong, Jingrui He
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.
Source: http://arxiv.org/abs/2412.21151v1
