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PyGCL/PyGCL ★ 967 98 APPLICATION

PyGCL is a PyTorch-based library for Graph Contrastive Learning (GCL) that provides modular components for building GCL models. The library implements graph augmentation techniques, contrasting architectures, contrastive objectives, and negative sampling strategies. It features clean abstractions that separate concerns across different graph frameworks (PyG and DGL), standardized evaluation utilities, and comprehensive examples demonstrating usage. The architecture follows a modular design with distinct modules for augmentors, losses, models, evaluation, and utilities, allowing users to mix and match components to build custom GCL algorithms.

RATING: 8.9/10 MAINT: active DRIFT: low
#graph-contrastive-learning #pytorch-library #graph-augmentation #contrastive-learning #self-supervised-learning #graph-neural-networks #reusable-components #modular-design #graph-representation-learning #machine-learning
▸ 5 reusable components found
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