#Graph Generation

Convert text to graphics using AI technology. SolidUI offers 2D and 3D graphic models and scenes by merging natural language processing with computer graphics. Its unique Vincent graph language model benefits from reinforcement learning for improved accuracy. The platform supports containerized deployment, various data sources, Huggingface collaboration, and plug-in robotics for enhanced visualization tool development.

DIG is a robust library designed to enhance the field of graph deep learning research, offering unified implementations and extensible frameworks. It stands out by supporting advanced research tasks like graph generation, self-supervised learning, and 3D graph analysis. DIG allows researchers to innovate and evaluate new methods alongside established ones using standard datasets and metrics. With the latest update built on PyTorch Geometric, it provides tools for diverse applications including explainability and graph augmentation, making it suitable for exploring graph neural networks and out-of-distribution issues.

GFlowNet is an innovative modeling framework optimized for discrete, combinatorial objects like graphs. The library centers on node-by-node graph construction via a graph neural network, which provides per-node and per-graph outputs. It supports multiple GFN algorithms and facilitates training with both existing and generated data. This resource offers detailed examples for practical application, such as molecule generation, and is compatible with Python 3.9+. It is available for installation through PIP, enabling flexible management of dependencies.