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Abdullah edited this page Jan 19, 2026
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Welcome to the GraphBrew wiki! This comprehensive guide will help you understand, use, and extend the GraphBrew framework for graph reordering and benchmark analysis.
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.
- 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
- Installation - System requirements and build instructions
- Quick-Start - Run your first benchmark in 5 minutes
- Supported-Graph-Formats - EL, MTX, GRAPH, and other formats
- Reordering-Algorithms - Complete guide to all 21 algorithms
- Graph-Benchmarks - PageRank, BFS, CC, SSSP, BC, TC explained
- Community-Detection - How Leiden clustering works
- Running-Benchmarks - Command-line usage and options
- Benchmark-Suite - Automated experiment runner
- Correlation-Analysis - Finding the best algorithm for your graphs
- AdaptiveOrder-ML - The perceptron-based algorithm selector
- Perceptron-Weights - Training and tuning the ML model
- GraphBrewOrder - Per-community reordering explained
- 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
- Command-Line-Reference - All flags and options
- Configuration-Files - JSON configs and settings
- Troubleshooting - Common issues and solutions
- FAQ - Frequently asked questions
# 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 3GraphBrew 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!
| Page | Description |
|---|---|
| Home | This page - wiki overview |
| Installation | Build requirements and instructions |
| Quick-Start | 5-minute getting started guide |
| Supported-Graph-Formats | EL, MTX, GRAPH format specs |
| Reordering-Algorithms | All 21 algorithms explained |
| Graph-Benchmarks | PR, BFS, CC, SSSP, BC, TC |
| Community-Detection | Leiden algorithm details |
| Running-Benchmarks | Manual benchmark execution |
| Benchmark-Suite | Automated experiment runner |
| Correlation-Analysis | Feature-algorithm correlation |
| AdaptiveOrder-ML | ML-based algorithm selection |
| Perceptron-Weights | Weight file format & tuning |
| GraphBrewOrder | Per-community hub ordering |
| Adding-New-Algorithms | Developer: add algorithms |
| Adding-New-Benchmarks | Developer: add benchmarks |
| Code-Architecture | Codebase structure |
| Python-Scripts | Analysis & utility tools |
| Command-Line-Reference | All CLI flags |
| Configuration-Files | JSON config reference |
| Troubleshooting | Common issues |
| FAQ | Frequently asked questions |
Last updated: January 2026