Erfaan-Rostami/Hypergraph-and-Graph-Neural-Network-HGNN-GNN--

This project is a legacy academic implementation with very low defensibility. Despite its age (nearly 7 years), it has garnered only 57 stars and 9 forks, indicating a lack of community adoption or maintenance. In the competitive landscape of Graph Neural Networks (GNNs), this project is superseded by robust, high-performance libraries like PyTorch Geometric (PyG), Deep Graph Library (DGL), and Spektral. These modern libraries offer highly optimized kernels, extensive documentation, and support for thousands of graph architectures. The claim in the description of 'proposing' GNNs is historically inaccurate for a 2018-era repo, as GNNs date back to the mid-2000s; it likely refers to a specific hypergraph variant (possibly related to the HGNN paper by Feng et al., 2019). Because frontier labs (OpenAI, DeepMind) and established platforms (Google, AWS via Neptune/DGL) have already integrated deep graph capabilities into their core stacks, a stale, non-library-grade repository like this holds zero commercial moat and has been effectively displaced for years.