Welcome to AnalysisG’s Documentation!

Contents:

AnalysisG - Graph Neural Network Analysis Framework

AnalysisG is a comprehensive Graph Neural Network Analysis Framework designed specifically for High Energy Physics (HEP) applications. The framework provides tools for event processing, graph construction, model development, and analysis in particle physics research.

Key Features

  • Event Processing: Flexible event processing pipeline for HEP data

  • Graph Neural Networks: Graph-based neural network architectures

  • Model Training: Complete training infrastructure with metrics

  • Analysis Tools: Comprehensive analysis and visualization tools

  • Python Integration: Seamless Python-C++ integration via Cython

Getting Started

To get started with AnalysisG:

  1. Check out the Introduction for an overview

  2. Browse the API Reference for detailed API documentation

  3. Explore Module Documentation to understand the framework architecture

  4. See Examples for usage examples

License

See LICENSE file in the repository root for licensing information.