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.
OpenHGNN is a comprehensive research framework for Heterogeneous Graph Neural Networks (HGNNs) built on PyTorch and DGL. The repository implements 83+ graph neural network models across 17+ tasks including node classification, link prediction, recommendation, knowledge graph completion, and temporal graph learning. It serves as a benchmarking platform for academic research, providing standardized implementations of state-of-the-art architectures like HGT, MAGNN, CompGCN, and HERO. The framework uses a registration pattern for dynamic model/trainer discovery and supports distributed training, hyperparameter optimization via Optuna, and GraphBolt integration for large-scale graphs. While it has CLI entry points and setup.py, its primary purpose is research reproducibility rather than production deployment. The codebase demonstrates strong architectural patterns with BaseDataset, BaseTask, and BaseFlow abstractions that enforce consistent interfaces across 50+ model implementations. However, significant technical debt exists including version mismatches (setup.py declares 0.7.0 while README claims 0.9.0), hardcoded dataset paths, mixed DGL/PyG usage, and monolithic configuration files. The repository contains extensive training artifacts in output/ directories and documentation drift between README, setup.py, and Sphinx docs. Despite these issues, it remains a valuable resource for graph learning researchers seeking reproducible implementations of heterogeneous GNN architectures.