https://github.com/sds-epfl/graphons.jl

Julia package for simple/decorated/probability graphons
https://github.com/sds-epfl/graphons.jl

graphons graphs networks random-graphs

Last synced: 6 months ago
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Julia package for simple/decorated/probability graphons

• Host: GitHub
• URL: https://github.com/sds-epfl/graphons.jl
• Owner: SDS-EPFL
• License: mit
• Created: 2023-02-03T13:39:32.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2025-10-16T00:53:38.000Z (8 months ago)
• Last Synced: 2025-10-16T02:02:39.141Z (8 months ago)
• Topics: graphons, graphs, networks, random-graphs
• Language: Julia
• Homepage: https://sds-epfl.github.io/Graphons.jl/
• Size: 2.19 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Graphons.jl

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://SDS-EPFL.github.io/Graphons.jl/stable/)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://SDS-EPFL.github.io/Graphons.jl/dev/)

[![Build Status](https://github.com/SDS-EPFL/Graphons.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/SDS-EPFL/Graphons.jl/actions/workflows/CI.yml?query=branch%3Amain)

[![Coverage](https://codecov.io/gh/SDS-EPFL/Graphons.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/SDS-EPFL/Graphons.jl)

Documentation for [Graphons.jl](https://github.com/SDS-EPFL/Graphons.jl) - A

Julia package for sampling random graphs from graphon models.

## Overview

Graphons are infinite-dimensional objects that represent the limit of large

graphs. This package provides tools to:

- Define graphon models (continuous functions or block models)

- Sample finite graphs from these models

- Work with decorated graphons that have rich edge attributes

## Quick Start

```julia

using Graphons

# Create a simple continuous graphon

g = SimpleContinuousGraphon((x, y) -> 0.3)

# Sample a random graph with 100 nodes

A = rand(g, 100)

# Create a stochastic block model

θ = [0.8 0.1; 0.1 0.8]  # High within-block, low between-block probability

sizes = [0.5, 0.5]       # Equal-sized blocks

sbm = SBM(θ, sizes)

A = rand(sbm, 200)

```

## Features

- **Simple Graphons**: Work with continuous probability functions on [0,1]²

- **Stochastic Block Models**: Discrete graphons with block structure

- **Decorated Graphons**: Rich edge attributes using Distributions.jl or custom

  distributions

## Installation

```julia

using Pkg

Pkg.add("Graphons")

```

## Documentation

For more details and tutorials please refer to the documentation.