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GraphBrew Wiki

Welcome to the GraphBrew wiki! This comprehensive guide will help you understand, use, and extend the GraphBrew framework for graph reordering and benchmark analysis.

🍺 What is GraphBrew?

GraphBrew is a high-performance graph reordering framework that combines community detection with cache-aware vertex reordering to dramatically improve graph algorithm performance. It implements over 20 different reordering algorithms and provides tools to automatically select the best one for your specific graph.

Key Features

• 21 Reordering Algorithms: From simple sorting to advanced ML-based selection
• Leiden Community Detection: State-of-the-art community detection for graph partitioning
• AdaptiveOrder: ML-powered perceptron that automatically selects the best algorithm
• Comprehensive Benchmarks: PageRank, BFS, Connected Components, Triangle Counting, and more
• Python Analysis Tools: Correlation analysis, benchmark automation, and weight training

📚 Wiki Contents

Getting Started

• Installation - System requirements and build instructions
• Quick-Start - Run your first benchmark in 5 minutes
• Supported-Graph-Formats - EL, MTX, GRAPH, and other formats

Understanding the Algorithms

• Reordering-Algorithms - Complete guide to all 21 algorithms
• Graph-Benchmarks - PageRank, BFS, CC, SSSP, BC, TC explained
• Community-Detection - How Leiden clustering works

Running Experiments

• Running-Benchmarks - Command-line usage and options
• Benchmark-Suite - Automated experiment runner
• Correlation-Analysis - Finding the best algorithm for your graphs

Advanced Topics

• AdaptiveOrder-ML - The perceptron-based algorithm selector
• Perceptron-Weights - Training and tuning the ML model
• GraphBrewOrder - Per-community reordering explained

Developer Guide

• Adding-New-Algorithms - Implement your own reordering method
• Adding-New-Benchmarks - Add new graph algorithms
• Code-Architecture - Understanding the codebase structure
• Python-Scripts - Analysis and utility scripts

Reference

• Command-Line-Reference - All flags and options
• Configuration-Files - JSON configs and settings
• Troubleshooting - Common issues and solutions
• FAQ - Frequently asked questions

🚀 Quick Example

# Build GraphBrew
make all

# Run PageRank with LeidenHybrid reordering
./bench/bin/pr -f your_graph.el -s -o 20 -n 3

# Let AdaptiveOrder choose the best algorithm
./bench/bin/pr -f your_graph.el -s -o 15 -n 3

📊 Performance Overview

GraphBrew typically achieves:

• 1.2-3x speedup on social networks (high modularity)
• 1.1-1.5x speedup on web graphs
• 1.0-1.2x speedup on road networks (low modularity)

The best algorithm depends on your graph's topology!

🔗 Quick Links

• GitHub Repository
• Paper/Citation (if applicable)
• Issue Tracker

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Last updated: January 2026