# ergm

**Repository Path**: li-tianping/ergm

## Basic Information

- **Project Name**: ergm
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: CC0-1.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-11
- **Last Updated**: 2024-05-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ergm
Python implementation of exponential random graph models

**DISCLAIMER:** This package is still under development and woefully incomplete. I'd say "use at your own risk" but for now, it's really more like, "don't use." You have been warned! 

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An exponential random graph model (ergm) is a probability distribution over graphs specified by a set of
_sufficient statistics_ and and corresponding parameters.

The probability density function takes the form

<img src="https://render.githubusercontent.com/render/math?math=P(G \mid \theta) = \frac{1}{Z}\exp\left(\sum_a \theta_a k_a(G)\right)">

Here, the k_a are the sufficient statistics (e.g. counts of edges, triangles, etc.) and the theta_a are the parameters.

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Eventually, this package will support sampling and estimation of parameters from data.