#graph deep learning

DGL provides a comprehensive Python library for deep learning on graphs, supporting frameworks like PyTorch, MXNet, and TensorFlow. Designed with scalability and performance in mind, it offers GPU-ready graph processing and a rich collection of GNN models and tutorials. DGL is widely used for graph-based machine learning with robust support for multi-GPU and distributed computing. It also includes a vibrant community for discussion, making it a valuable resource for both novice and experienced researchers.

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.

CogDL provides an open-source toolkit for efficient graph deep learning, facilitating speedy model training and efficient GPU usage. Its intuitive APIs support diverse experiments including node and graph classification. Recent enhancements include mixed-precision training, self-supervised learning, and a broad dataset library, making it ideal for deploying GNN models in various applications.

Spektral offers a comprehensive framework for developing graph neural networks using Python’s Keras and TensorFlow 2, with tasks including social network user classification and molecular property prediction. The 1.0 release simplifies data handling through new Graph and Dataset containers, a simplified Loader class, and versatile GNN classes, supported by extensive examples and documentation.