Welcome to MMSBM’s documentation!

Contents:

• Installation Guide
  • Requirements
  • Basic Installation
  • Installing Optional Backends
  • Development Installation
  • Verifying Installation
  • Running Tests
  • Building Documentation
  • Troubleshooting
  • Getting Help
• Quick Start Guide
  • Basic Usage
  • Complete Example
  • Cross-Validation
  • Model Parameters
  • Additional Features
  • Next Steps
• Mixed Membership Stochastic Block Model
  • Introduction to MMSBM
  • Model Structure
  • Mathematical Formulation
  • Model Training
  • Making Predictions
  • Interpreting Results
  • Want to Learn More?
• Expectation-Maximization Algorithm
  • The Problem
  • The Challenge
  • Key Variables and Notation
  • The Algorithm
  • Parameter Normalization
  • Performance Optimizations
  • Want to Learn More?

MMSBM (Mixed Membership Stochastic Block Models) is a Python library for efficient recommendation systems using stochastic block modeling.

Quick Links

• Installation Guide

• Quick Start Guide

• Mixed Membership Stochastic Block Model

• Index

• Module Index

• Search Page