Welcome to GSNN’s Documentation!

The Graph Structured Neural Networks (GSNN) library provides flexible and scalable tools for learning on graph-structured data.

Overview

• Introduction
  • What is GSNN?
  • Key Concepts
  • Why Use GSNN?
  • Core Features
  • How Are GSNNs Different from Graph Neural Networks?
  • Getting Started
  • Installation
  • Citation
  • Next Steps
• Methods
  • Graph Structured Neural Network (GSNN)
• Explainers
  • Edge Attribution Methods
  • Direct Perturbation Methods
  • Optimization-Based Methods
  • Robustness and Stability Methods
  • Choosing the Right Explainer

Tutorials

• General Premise
• Simulating structured data
• Performance comparison on simulated data
• Reinforcement learning for structure optimization
• Gradient checkpointing and compiling
• Uncertainty quantification with hypernetworks
• GSNN Interpretation methods
• DrugCell implementation example
• Inferring output edges

API Reference

• API Reference

Indices and Tables

• Index

• Module Index

• Search Page