#PyTorch Geometric

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.

This open-source framework leverages PyTorch Geometric and Facebook Hydra to facilitate the deployment of deep learning models on 3D point cloud data. It supports model development with features like mixed precision training, high-level API access, and methods including PointNet and PVCNN for tasks such as segmentation and object detection. The framework offers a streamlined setup process with Docker and supports diverse datasets such as ScanNet and ModelNet.

This project offers a range of benchmark datasets, data loaders, and standardized evaluators for graph machine learning tasks across various domains. Compatible with PyTorch Geometric and Deep Graph Library, it supports prediction tasks at node, link, and graph levels. The repository ensures easy dataset access with standardized splits and performance evaluation, suitable for both small and large-scale graphs. Continuous efforts are being made to expand its offerings for advanced graph machine learning. Installation is streamlined via pip, prioritizing version consistency.

PyGOD is a Python library for graph outlier detection, featuring over 10 algorithms for anomaly detection in networks and security systems. Utilizing PyTorch and PyTorch Geometric bases, it provides a smooth API, comprehensive documentation, and versatile examples for node, edge, and graph-level tasks. PyGOD is designed for scalability and efficiently handles large graphs through mini-batch processing and sampling, enabling rapid outlier detection with concise code. The ongoing updates offer optimal performance and solid community backing from renowned research institutions.

TypeDB-ML provides integration options for graph algorithms and machine learning within TypeDB, utilizing tools like NetworkX and PyTorch Geometric. It supports building customizable Graph Neural Networks for data in TypeDB and facilitates efficient graph-based data manipulation for link prediction. The platform offers diverse querying and encoding features, complemented by resources for effective data management within TypeDB. Learn through practical examples about maximizing data utility with Graph Neural Networks.