# NetworKit: graph algorithms for networks with billions of edges

> NetworKit is a C++ toolkit for large-scale network analysis with Python bindings exposed through Cython, offering parallel implementations of community detection, centrality and component algorithms. It suits a researcher or engineer who needs graph measures on graphs that will not fit in a Python list.

**networkit/networkit** — NetworKit is a growing open-source toolkit for large-scale network analysis.

- Repository: https://github.com/networkit/networkit
- Website: https://networkit.github.io
- Stars: 881 · Forks: 258
- Language: C++
- License: MIT
- Published: 2026-10-20 · Updated: 2026-10-20 · Language: en
- Canonical page: https://hysenlabs.com/projects/networkit-networkit

## The scale claim, and what sits underneath it

The positioning is about scale, and the number used is specific. NetworKit is an open-source toolkit for high-performance network analysis, designed to handle large networks ranging from thousands to billions of edges, and the key features list opens with a scalability claim about networks with billions of edges. What makes that possible is the architecture, and the documentation is direct about it. The toolkit implements parallel graph algorithms that use multicore architectures, and it 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. The framing of the project is dual purpose, and it matters for expectations. It is described as both a production tool and a research testbed for algorithm engineering, and it says it includes novel algorithms from recent publications alongside battle-tested implementations. So the same library can be the thing you ship and the thing you prototype a new centrality measure in.

## Installing, and the separate C++ library

For most users the Python module installs from a package manager with no additional requirements other than Python 3.10 or newer:

```bash
pip install networkit
```

The alternatives are conda from the conda-forge channel, Homebrew and Spack, and the Spack install targets the py-networkit package. If you only need the C++ core without the Python bindings, the same three package managers are listed with a libnetworkit package name, which is the cleanest way to use the algorithms from a C++ project without pulling in a Python interpreter. There is also a nightly channel, published on anaconda.org under the networkit-nightly channel, and the pip command for it uses an extra index URL rather than a release tag. The documentation explains what the nightly contains, with published packages based on pushes to the master branch and the channel keeping the five most recent ones. A nightly channel for a C++ project is a serious tool, because a change in a C++ core can alter results, and it is worth remembering that a nightly install is a different contract from a versioned one.

## The example workflow, and what the output tells you

The usage example is worth reading as a shape rather than as a recipe, because the pattern repeats throughout the library. You import a generator and an algorithm from separate submodules, you build the object, call a method that returns a configured instance, and then run it:

```python
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)
```

The inspect flag is what makes this usable interactively, because it prints the solution properties. The documented output for that run shows the algorithm that was chosen, a run time of about 0.15 seconds, and a table of properties including the number of communities, the minimum and maximum community size, the average community size and a modularity value close to 0.99. Two things are worth noticing. The result reports which algorithm actually ran, which is important when a library offers several implementations of the same measure, and the modularity figure tells you the partition is meaningful rather than arbitrary. The graph is a random hyperbolic one, which is a model of a network with a small number of hubs and a tree-like periphery, and it is a standard stress test for community detection because those communities are genuinely present in the structure rather than being artefacts of the algorithm.

## Centrality and components, and the same two-step shape

The other two examples use the same construction, which is the API convention to learn. Centrality is imported from a centrality submodule, constructed with the graph, and run:

```python
from networkit.centrality import PageRank

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

Connected components work identically, with the result exposing the component count and the sizes:

```python
from networkit.components import ConnectedComponents

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

The pattern is construct, run, query, and every algorithm in the library follows it, which means the cost of learning the library is learning one convention rather than one API per algorithm. The choice of PageRank in the example is also telling, since it is the most familiar centrality measure and the one a newcomer reaches for first, and the ranking slice shows that the result is a ranked list rather than a value. The documentation points to interactive notebooks for more examples and tutorials, in particular the NetworKit User Guide, and to a Binder instance so you can try it in the browser without installing anything.

## Building from source, and the compiler floor

Building the project yourself needs more than Python. The prerequisites are a C++ compiler, and the requirement is stated as g++ at version 10.0 or newer, clang++ at 11.0 or newer, or MSVC at 14.30 or newer, plus OpenMP for the parallelism, which is usually included with the compiler, plus Python 3.10 or newer with development libraries, with the per-distribution commands given for Debian and Ubuntu and for RHEL and CentOS. The Python packaging reflects the C++ core rather than hiding it. The build system requires Cython at a version below 3.3.0 along with cmake, numpy, scipy, setuptools and wheel, and the setup script itself compiles the extension modules with Cython and shells out to cmake, make and cython. It also honours a set of environment variables, including one that overrides the C++ compiler, one that enables an OSX cross build, and one that turns on building the tests, plus a parallel jobs setting that defaults to the number of processors. The runtime dependencies of the Python module are modest, with scipy, numpy and tabulate, and the development requirements add seaborn, scikit-learn, matplotlib, pandas, networkx and ipython.

## The notebook image, and what the packaging says about the project

The Docker image in the repository is a Jupyter base notebook image, and its comments are unusually honest about why. The version pinning of the base image is described as temporary, due to errors when using the latest, in the form of a kernel version error triggered when calling conda. The image installs cmake, git and build-essential, then pip installs setuptools, cython, powerlaw, scikit-learn, seaborn, pandas, tabulate, matplotlib, networkx and ipycytoscape, and it builds the current branch of the project in editable mode with the extension build step, then installs the Jupyter lab extensions for the widget manager and for cytoscape. The reason the image exists is the interactive side of the project, and it points at the notebooks directory, which is where the examples live, including a user guide notebook that is also available through a Binder link. The package metadata says the development status is production and stable, and the licence file is referenced from the project configuration, with the project itself being MIT licensed. The repository carries a citation file and a changelog, and the authors are listed as Christian L. Staudt and Henning Meyerhenke.

## Conclusion

NetworKit is a good fit for a computational social science or network science team whose graphs are too large for a pure Python analysis, because the algorithms are implemented in C++ with OpenMP parallelism and exposed through a Python interface that keeps the analysis code short. It is the wrong choice if you need graph analytics on small graphs, where NetworkX or igraph will be quicker, and it is the wrong choice if you need a maintained release stream, since the repository publishes no GitHub releases and the published packages track pushes to the master branch. It is also the wrong choice if you cannot compile, since the C++ core is not optional. Before adopting it, check the compiler requirements, where the C++ compiler needs to be reasonably recent and OpenMP is required for the parallelism, and pin the version rather than tracking the nightly channel except when you need the newest algorithms. The project is MIT licensed, the last push was on 2026-10-17, and the Python module requires Python 3.10 or newer.

## FAQ

### What is NetworKit and what scale does it handle?

It is an open-source toolkit for high-performance network analysis, designed to handle large networks ranging from thousands to billions of edges. It implements parallel graph algorithms that leverage multicore architectures, and the key features list describes it as scalable, fast, comprehensive, interactive, flexible and research-ready.

### How do I install NetworKit?

The Python module installs with pip install networkit and needs Python 3.10 or newer, with conda, Homebrew and Spack also offered. If you only need the C++ core without Python bindings, the same managers carry a libnetworkit package. A nightly channel on anaconda.org keeps the five most recent builds from pushes to the master branch.

### How do I run a community detection algorithm in NetworKit?

Import the generator and the algorithm from their submodules, build the graph, construct the algorithm with the graph, call run, and pass inspect to print the solution properties. The documented example detects communities in a random hyperbolic graph of 100k nodes and reports the algorithm used, a run time, the community count and a modularity value.

### What do I need to build NetworKit from source?

A reasonably recent C++ compiler, listed as g++ 10.0 or newer, clang++ 11.0 or newer, or MSVC 14.30 or newer, plus OpenMP for the parallelism, plus Python 3.10 or newer with development libraries. The build system uses Cython, cmake, numpy, scipy, setuptools and wheel, and the setup script honours environment variables for the compiler, the tests and the parallel job count.

### Is there a browser-based way to try NetworKit?

Yes. The documentation links a Binder instance that runs the user guide notebook, and the repository ships a Docker image based on a Jupyter notebook image that installs the C++ extension from the current branch along with the Jupyter lab extensions for cytoscape. The project is MIT licensed and the last push was on 2026-10-17.

## Sources

- [Issues](https://github.com/networkit/networkit/issues)
- [License: MIT](https://github.com/networkit/networkit/blob/master/LICENSE)
- [networkit/networkit on GitHub](https://github.com/networkit/networkit)
- [Project website](https://networkit.github.io)
- [README](https://github.com/networkit/networkit/blob/master/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/networkit-networkit