PyG-SSL – An open source library for self-supervised learning of graphs and supports various scientific computing and deep learning backends

Complex domains such as social networks, molecular biology, and recommender systems have data structured in graphs consisting of nodes, edges, and their respective characteristics. These nodes and edges have no structured relationship, so it is essential to address them using graph neural networks (GNN). However, GNNs rely on labeled data, which is difficult and expensive to obtain. Self-supervised learning (SSL) is an evolving methodology that leverages unlabeled data by generating its supervisory signals. SSL for graphics presents its own challenges, such as domain specificity, lack of modularity, and a steep learning curve. In addressing these questions, A team of researchers from the University of Illinois Urbana-Champaign, Wayne State University, and Meta ai have developed PyG-SSL, an open source toolset designed to advance self-supervised learning of graphs.

Current graph self-supervised learning (GSSL) approaches mainly focus on pretexting (self-generated) tasks, graph augmentation, and contrastive learning. Pretext includes node-level, edge-level, and graph-level tasks that help the model learn useful representations without requiring labeled data. Its augmentation occurs by deleting, masking or shuffling, improving the robustness and generalization of the model. However, existing GSSL frameworks are designed for specific applications and require significant customization. Additionally, developing and testing new SSL methods is time-consuming and error-prone without a modular and extensible framework. Therefore, a new process is needed to address the fragmented nature of existing GSSL implementations and the absence of a unified toolset that restricts standardization and benchmarking between various GSSL methods.

The proposed toolkit, PyG-SSL, standardizes the implementation and evaluation of graph SSL methods. The key features of PyG-SSL are:

• Comprehensive support: This toolkit integrates multiple state-of-the-art methods for a unified framework, allowing researchers to select the most suitable method for their specific application.
• Modularity: PyG-SSL allows the creation of custom solutions by mixing one or more techniques. Pipes can also be customized without requiring extensive reconfiguration.
• Benchmarks and Data Sets: Standard data sets and evaluation protocols are preloaded in this toolkit to allow researchers to easily compare their findings and ensure validation.
• Performance Optimization: The PyG-SSL toolkit is designed to handle large data sets efficiently. It is optimized for fast training time and low computational requirements.

This toolset has been rigorously tested on multiple SSL data sets and methods, demonstrating its effectiveness in standardizing and advancing graph SSL research. With reference implementations of a wide range of SSL methods, PyG-SSL ensures that results are reproducible and comparable across experiments. Experimental results demonstrate that integrating PyG-SSL into existing GNN architectures improves their performance in downstream tasks by properly exploiting unlabeled data.

PyG-SSL marks an important milestone in self-supervised graph learning, addressing long-standing challenges related to standardization, reproducibility, and accessibility. PyG-SSL provides the ability to achieve state-of-the-art results through its unified, modular, and extensible toolset, facilitating the development of innovative graph SSL methods. PyG-SSL can play a critical role in advancing graph-based machine learning applications in various domains in this rapidly evolving field.

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