digraphx 0.5

Network Optimization Python Code

🔀 digraphx

Efficient directed graph algorithms for network optimization in Python

digraphx is a Python library that provides high-performance implementations of directed graph algorithms, optimized for large-scale network optimization problems. It extends NetworkX with specialized data structures and algorithms for negative cycle detection, cycle ratio optimization, and parametric network analysis.

Note that digraphx does not directly handle multi-edges (same as networkx)

Features

• High-performance graph storage: TinyDiGraph with memory-efficient MapAdapter backend
• Negative cycle detection: Howard's algorithm for finding negative cycles in weighted directed graphs
• Minimum/Maximum Cycle Ratio: Efficient algorithms for finding cycles with optimal cost/time ratios
• Parametric network optimization: Generic framework for ratio-based optimization problems
• Type-safe: Full type hints throughout codebase
• Well-tested: Comprehensive test coverage with pytest

When to Use digraphx vs NetworkX

Use digraphx when:

• Working with very large graphs (thousands to millions of nodes)
• Need memory-efficient storage for graphs with known node counts
• Optimizing cycles based on cost/time ratios
• Finding negative cycles in weighted graphs
• Implementing parametric optimization algorithms

Use standard NetworkX when:

• Need a general-purpose graph library
• Working with small to medium-sized graphs
• Need extensive graph algorithms not in digraphx
• Dynamic graph operations (frequent node/edge additions/removals)

Installation

From PyPI

pip install digraphx

From Source

git clone https://github.com/luk036/digraphx.git
cd digraphx
pip install -e .

Dependencies

• NetworkX - Graph library foundation
• luk036/mywheel - Custom utilities (MapAdapter for efficient storage)

Quick Start

Detect Negative Cycles

from digraphx import NegCycleFinder

# Create a directed graph with a negative cycle
digraph = {
    'a': {'b': 7, 'c': 5},
    'b': {'a': 0, 'c': 3},
    'c': {'a': 2, 'b': 1}
}

# Initialize distances
dist = {node: 0 for node in digraph}

# Find negative cycles using Howard's algorithm
finder = NegCycleFinder(digraph)
for cycle in finder.howard(dist, lambda edge: edge):
    print(f"Found negative cycle: {cycle}")

Minimum Cycle Ratio

from digraphx import MinCycleRatioSolver
from digraphx import DiGraphAdapter
from fractions import Fraction

# Create a graph with cost and time attributes
graph = DiGraphAdapter()
graph.add_edge('a', 'b', cost=5, time=1)
graph.add_edge('b', 'c', cost=3, time=1)
graph.add_edge('c', 'a', cost=-2, time=1)

# Solve for minimum cycle ratio
solver = MinCycleRatioSolver(graph)
dist = {node: Fraction(0) for node in graph}
ratio, cycle = solver.run(dist, Fraction(10))
print(f"Minimum cycle ratio: {ratio}")

High-Performance Graph with TinyDiGraph

from digraphx import TinyDiGraph

# Create a graph optimized for 1000 nodes
graph = TinyDiGraph()
graph.init_nodes(1000)

# Add edges efficiently
for i in range(1000):
    for j in range(i+1, min(i+10, 1000)):
        graph.add_edge(i, j, weight=i+j)

# Access graph properties
print(f"Nodes: {graph.number_of_nodes()}")
print(f"Edges: {graph.number_of_edges()}")

Algorithms Implemented

Core Algorithms

• Howard's Algorithm: Efficient negative cycle detection using policy iteration
• Bellman-Ford: Shortest paths with negative edge weights
• Parametric Optimization: Generic framework for ratio-based objectives
• Cycle Ratio: Find cycles with minimum/maximum cost/time ratios

Data Structures

• TinyDiGraph: Memory-efficient directed graph for fixed-size node sets
• DiGraphAdapter: NetworkX compatibility layer with custom storage
• MapAdapter: List-based dictionary for O(1) node/edge access

Documentation

Full API documentation is available at https://digraphx.readthedocs.io/

Testing

Run's test suite:

# Run all tests
pytest

# Run specific test file
pytest tests/test_tiny_digraph.py

# Run with coverage
pytest --cov=digraphx --cov-report=html

Development

# Install development dependencies
pip install -e ".[testing]"

# Run linting
pre-commit run --all-files

# Format code
black src/digraphx tests
isort src/digraphx tests

# Build package
tox -e build

Contributing

Contributions are welcome! Please ensure:

• All tests pass
• Code follows the project's style guidelines (see AGENTS.md)
• New features include tests and documentation
• Pre-commit hooks pass before pushing

License

This project is licensed under the MIT License - see the LICENSE.txt file for details.

Acknowledgments

Built on top of NetworkX Uses PyScaffold for project structure

Contact

• Author: Wai-Shing Luk
• Email: [e-mail]
• GitHub: @luk036

Citation

If you use digraphx in your research, please cite:

@software{digraphx,
  title = {digraphx: Directed Graph X in Python},
  author = {Luk, Wai-Shing},
  url = {https://github.com/luk036/digraphx},
  year = {2026}
}