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.
This repository is a personal collection of study notes and PDF papers focused on large language models for recommendation systems (LLM4Rec). It contains markdown notes summarizing topics such as SNS-based LLM post-training, vocabulary customization for LLMs, LLM-to-SQL techniques, and algorithm explanations (e.g., longest palindrome substring). The majority of files are PDF research papers and reference materials, with no source code, configuration, or executable components. The content serves as a reference library for the maintainer's learning and interview preparation.