Welcome to egraph documentation

egraph is a Python library for graph visualization and layout algorithms, providing efficient implementations of various graph algorithms in Rust with Python bindings.

Contents:

• Getting Started
  • Overview
    • What is egraph?
    • Key Features
    • Use Cases
    • Architecture
    • Philosophy
    • Next Steps
  • Installation
    • Basic Installation
    • Requirements
    • Optional Dependencies
    • Development Installation
    • Verifying Installation
    • Next Steps
  • Quick Start
    • Your First Graph Layout
    • Working with NetworkX
    • Using Different Layout Algorithms
    • Key Concepts
    • Next Steps
  • Overview
  • Installation
  • Quick Start
• Tutorial
  • Graph Basics
    • Creating Graphs
    • Accessing Graph Data
    • Working with NetworkX
    • Directed Graphs
    • Best Practices
    • Next Steps
  • Layout Algorithms
    • Understanding Layout Algorithms
    • Stress Majorization
    • Kamada-Kawai
    • SGD (Stochastic Gradient Descent)
    • MDS (Multidimensional Scaling)
    • Choosing the Right Algorithm
    • Customizing Edge Lengths
    • Next Steps
  • Drawing and Visualization
    • Understanding Drawing Spaces
    • Euclidean 2D Drawing
    • N-Dimensional Euclidean Drawing
    • Hyperbolic 2D Drawing
    • Spherical 2D Drawing
    • Torus 2D Drawing
    • Visualization with Matplotlib
    • Choosing the Right Drawing Space
    • Best Practices
    • Next Steps
  • Learning Path
  • What You’ll Learn
  • Prerequisites
  • Next Steps
• API Reference
  • Graph Module
    • Graph
    • DiGraph
    • GraphAdapter
  • Drawing Module
    • DrawingEuclidean2d
    • DrawingEuclidean
    • DrawingSpherical2d
    • DrawingHyperbolic2d
    • DrawingTorus2d
  • Layout Module
    • FullSgd
    • SparseSgd
    • Schedulers
    • StressMajorization
    • KamadaKawai
    • ClassicalMds
    • PivotMds
    • OverwrapRemoval
  • Algorithm Module
    • Shortest Path Algorithms
  • Quality Metrics Module
    • Stress
    • Crossing Number
    • Neighborhood Preservation
    • Aspect Ratio
    • Angular Resolution
    • Node Resolution
    • Gabriel Graph Property
  • Distance Matrix Module
    • DistanceMatrix
  • Random Number Generation Module
    • Rng
• Examples
  • Stochastic Gradient Descent (SGD)
    • Basic SGD Example
    • Using FullSgd
  • Stress Majorization
    • Basic Stress Majorization Example
    • Using a Distance Matrix
    • Applying a Single Iteration
  • Kamada-Kawai
    • Basic Kamada-Kawai Example
    • Using Custom Edge Distances
    • Applying to a Single Node
  • 3D Stochastic Gradient Descent
    • Basic 3D SGD Example
    • Using ClassicalMds for 3D Initialization
  • Hyperbolic 2D Stochastic Gradient Descent
    • Basic Hyperbolic SGD Example
    • Working with Hyperbolic Distances
  • Spherical 2D Stochastic Gradient Descent
    • Basic Spherical SGD Example
    • Working with Spherical Distances
  • Torus 2D Stochastic Gradient Descent
    • Basic Torus SGD Example
    • Visualizing the Torus in 3D
  • Overwrap Removal
    • Basic Overwrap Removal Example
    • Using Variable Node Sizes
    • Controlling the Overlap Removal Process
  • Overview
  • Layout Algorithms
  • Advanced Drawing Spaces
  • Specialized Features
  • Quick Example

Features

• Graph data structures (Graph, DiGraph)

• Drawing spaces (Euclidean, Hyperbolic, Spherical, Torus)

• Layout algorithms:

  • Stochastic Gradient Descent (SGD)

  • Multidimensional Scaling (MDS)

  • Stress Majorization

  • Kamada-Kawai

  • Overlap Removal

• Quality metrics for evaluating layout effectiveness

• Integration with NetworkX

Indices and tables

• Index

• Search Page