HHW-zhou/TDHNN

TDHNN is a standard academic research artifact. With only 25 stars and 1 fork over nearly three years, it has failed to transition from a paper-supporting codebase to a used library or framework. In the competitive landscape of Graph Neural Networks (GNNs), this project sits far below infrastructure-grade libraries like PyTorch Geometric (PyG) or Deep Graph Library (DGL), which offer much broader hypergraph support and better optimization. The 'totally dynamic' aspect is a specific research niche; while the math may be novel, the code is a static implementation that lacks the velocity or community to serve as a moat. Frontier labs are unlikely to compete here directly, as their focus remains on Large Language Models (LLMs) and broad multimodal architectures, leaving specialized graph modeling to academia and niche industrial labs. The project is easily displaced by more modern implementations or by the integration of similar dynamic hypergraph modules into major GNN frameworks. The displacement horizon is set at 1-2 years, as newer research papers often supersede these specific architectures within that timeframe.