NetworKit

High-performance tools for large-scale network analysis
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Table of Contents

1. About The Project
   • Key Features
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Building from Source
5. Documentation
6. Contributing
7. License
8. Contact
9. Publications

About The Project

NetworKit is an open-source toolkit for high-performance network analysis, designed to handle large networks ranging from thousands to billions of edges. Built with efficiency and scalability at its core, NetworKit implements parallel graph algorithms that leverage multicore architectures to compute standard measures of network analysis.

As both a production tool and a research testbed for algorithm engineering, NetworKit includes novel algorithms from recent publications alongside battle-tested implementations. The toolkit is available as a Python module with high-performance C++ algorithms exposed through Cython, combining Python's interactivity and rich ecosystem with C++'s computational efficiency.

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Key Features

• Scalable: Analyze networks with billions of edges
• Fast: Parallel algorithms utilizing multicore architectures
• Comprehensive: Wide range of network analysis algorithms
• Interactive: Python interface with Jupyter notebook support
• Flexible: Available as Python module or standalone C++ library
• Research-ready: Includes state-of-the-art algorithms from recent publications

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Getting Started

Installation

Python Module

For most users, NetworKit can be installed directly via package managers with no additional requirements other than Python 3.9+.

Package Manager	Command
pip	pip install networkit
conda	conda install -c conda-forge networkit
brew	brew install networkit
spack	spack install py-networkit

C++ Core Library Only

If you only need the C++ core without Python bindings:

Package Manager	Command
conda	conda install -c conda-forge libnetworkit
brew	brew install libnetworkit
spack	spack install libnetworkit

More platform-specific installation instructions can be found in our getting started guide.

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Usage

Here's a quick example showing how to generate a random hyperbolic graph with 100k nodes and detect communities:

from networkit.generators import HyperbolicGenerator
from networkit.community import detectCommunities

# Generate a random hyperbolic graph
g = (
    HyperbolicGenerator(1e5)
    .generate()
)

# Detect communities
detectCommunities(g, inspect=True)

Output:

PLM(balanced,pc,turbo) detected communities in 0.14577102661132812 [s]
solution properties:
-------------------  -----------
# communities        4536
min community size      1
max community size   2790
avg. community size    22.0459
modularity              0.987243
-------------------  -----------

More Examples

Compute PageRank to rank nodes by importance:

from networkit.centrality import PageRank

pr = (
    PageRank(g)
    .run()
)
top_nodes = pr.ranking()[:10]

Analyze graph structure with connected components:

from networkit.components import ConnectedComponents

cc = (
    ConnectedComponents(g)
    .run()
)
print(f"Components: {cc.numberOfComponents()}")
print(f"Largest: {max(cc.getComponentSizes().values())}")

For comprehensive examples and tutorials, explore our interactive notebooks, especially the NetworKit User Guide. You can try NetworKit directly in your browser using our Binder instance.

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Building from Source

Prerequisites

Building from source requires:

• C++ Compiler: g++ (>= 10.0), clang++ (>= 11.0), or MSVC (>= 14.30)
• OpenMP: For parallelism (usually included with compiler)
• Python: 3.9 or higher with development libraries
  • Debian/Ubuntu: apt-get install python3-dev
  • RHEL/CentOS: dnf install python3-devel
  • Windows: Official installer
• CMake: Version 3.6 or higher
• Build System: Make or Ninja

Python Module

git clone https://github.com/networkit/networkit networkit
cd networkit
pip install cython numpy setuptools wheel
python setup.py build_ext [-jX]
pip install -e .

The -jX option specifies the number of threads for compilation (e.g., -j4 for 4 threads). If omitted, it uses all available CPU cores.

C++ Core Library

mkdir build && cd build
cmake ..
make -jX
sudo make install

Using NetworKit in Your C++ Project

After installation, include NetworKit headers:

#include <networkit/graph/Graph.hpp>

Compile your project:

g++ my_file.cpp -lnetworkit

Running Unit Tests

To build and run tests:

cmake -DNETWORKIT_BUILD_TESTS=ON ..
make
./networkit_tests --gtest_filter=CentralityGTest.testBetweennessCentrality

Building with Sanitizers

For debugging with address/leak sanitizers:

cmake -DNETWORKIT_WITH_SANITIZERS=leak ..

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Documentation

The complete documentation is available online at networkit.github.io.

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Contributing

We welcome contributions to NetworKit! Whether you're fixing bugs, adding features, or improving documentation, your help makes NetworKit better for everyone.

1. Check our development guide for instructions
2. Browse open issues or open a new one
3. Fork the repository and create your feature branch
4. Submit a pull request

For support, join our mailing list.

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License

Distributed under the MIT License. We ask that you cite us if you use NetworKit in your research (see our technical report and publications page).

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Contact

• Issues: Check our issues section for existing discussions or open a new issue
• Mailing List: Subscribe here to stay updated

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Publications

NetworKit has been used in numerous research projects. Visit our publications page for a complete list of papers about NetworKit, algorithms implemented in NetworKit, and research using NetworKit.

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Credits

NetworKit is developed by a dedicated team of researchers and contributors. View the full list of contributors on our credits page.

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