Shypd.aiThe largest open index of AI toolsPyGCL is an open-source PyTorch library for Graph Contrastive Learning, offering modular components for GCL algorithms. It provides standardized evaluation and experiment management for graph representation learning.
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PyGCL is a PyTorch-based open-source library specifically designed for Graph Contrastive Learning (GCL). It provides a comprehensive framework for researchers and developers to implement and experiment with various GCL algorithms. The library features modularized GCL components, including graph augmentation techniques like Edge Adding, Feature Masking, and Node Dropping, as well as different contrasting architectures and modes (single-branch, dual-branch, bootstrapped, within-embedding). PyGCL also implements a variety of contrastive objectives such as InfoNCE, JSD, and Barlow Twins, alongside negative sampling strategies. It supports standardized evaluation with evaluators like Logistic Regression and SVM, and offers utilities for managing experiments, making it a valuable tool for advancing graph representation learning.
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