fork of repository: NetworKit Implementation of the nearly-linear time SDD solver developed by Kelner et al. and described in Implementation and experimental results to appear in SEA 2015, preprint:
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fixed dependency on valgrind 2 years and 2 months ago mwegner (Michael Wegner)
fixed dependency on valgrind 2 years and 2 months ago mwegner (Michael Wegner)
Merge from Dev 2 years and 9 months ago Daniel Hoske
suppress output of test compilation 2 years and 9 months ago maxv (Maximilian Vogel)
community.PLP can be asked for timings of each ... 2 years and 9 months ago cls (Christian Staudt)
added GraphToolBinary{Writer,Reader} and corres... 2 years and 9 months ago maxv (Maximilian Vogel)
cythonized GraphToolBinary{Reader,Writer} 2 years and 9 months ago maxv (Maximilian Vogel)
added rudimentary support for graph-tool's bina... 2 years and 9 months ago maxv (Maximilian Vogel)
set default amount of threads during installati... 2 years and 9 months ago maxv (Maximilian Vogel)
added Cython version check; better attempt at d... 2 years and 9 months ago maxv (Maximilian Vogel)
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NetworKit is a growing open-source toolkit for high-performance network analysis. Its aim is to provide tools for the analysis of large networks in the size range from thousands to billions of edges. For this purpose, it implements efficient graph algorithms, many of them parallel to utilize multicore architectures. These are meant to compute standard measures of network analysis, such as degree sequences, clustering coefficients and centrality (see next sections of this document for a list of existing features and a roadmap of planned features). In this respect, NetworKit is comparable to packages such as [NetworkX][networkx], albeit with a focus on parallelism and scalability. NetworKit is also a testbed for algorithm engineering and contains novel algorithms from recently published research, especially in the area of community detection (see list of publications below).

NetworKit is a Python module. High-performance algorithms are written in C++ and exposed to Python via the Cython toolchain. Python in turn gives us the ability to work interactively and a rich environment of tools for data analysis and scientific computing. Furthermore, NetworKit's core can be built and used as a native library if needed.



In addition to this Readme, the NetworKit_UserGuide provides an introduction to the NetworKit tools, in the form of an interactive IPython Notebook. The DevGuide is meant for developers who would like to contribute. When using NetworKit as a Python module, refer to the docstrings of classes, methods and functions.

C++ sources are also documented in Doxygen format, while the documentation for the Python sources can be generated with Sphinx. If you have both utilities installed, the documentation can be easily generated by calling the script in Doc/docs.

To convert the documentation markdown files to PDF install the pandoc utility and call the script

E-Mail List

For questions regarding NetworKit, subscribe to our e-mail list ( and feel free to ask.



A C++ compiler supporting C++11 (we use GCC 4.8). The compiler and linker flags -fopenmp -std=c++11 are required.


  • OpenMP for parallelism

To avoid possible binary incompatibilities, try to build these libraries with the same compiler that will be used to build NetworKit.

Building NetworKit as a Python Module

To build the module, the following is required:

  • Python 3 (>= 3.4 recommended, 3.3 supported)
  • Cython

Additionally, the module uses the following external Python packages:

  • networkx
  • tabulate
  • scipy
  • matplotlib

NetworKit is still usable without these packages, but with reduced functionality.The dependencies are best installed via easy_install or pip. (If you have multiple Python installations, be sure you use the commands matching your Python 3 version. In the following examples, python3 and pip3 will be used.)

pip3 install package_name

Then, switch to the top folder and run the script with the following options:

python3 build_ext --inplace [--optimize=V] [-jX]

The script will call scons to compile NetworKit as a library and then build the extensions in the top folder. By default, NetworKit will be built with the amount of available cores in optimized mode. It is possible the add the options --optimize=V and -jN the same way it can be done to a manual scons call, to specify the optimization level and the number of threads used for compilation. The setup script provides more functionality:

python3 develop [--uninstall] [--optimize=V] [-jX]

will compile NetworKit, build the extensions and on top of that temporarily install NetworKit so that it is available on the whole system. This can be undone by adding --uninstall.

python3 clean [--optimize=V]

will remove the extensions and its build folder as well as call scons to remove the NetworKit library and its build folder specified by --optimize=V.

Note: All of the above installation command may require sudo privileges depending on your system, so try this accordingly.

Assuming the extension module _NetworKit exists, it can be imported in python:

>>> import networkit

Interactive Work with NetworKit

With NetworKit as a Python extension module, you get access to native high-performance code and can at the same time work interactively in the Python ecosystem. Although the standard Python interpreter works fine, we recommend IPython as a great environment for scientific computing. IPython can also be installed via pip or easy_install. For tab completion in ipython you may also need to install readline. The following should work: pip3 install readline ipython.

After the requirements are satisfied, start IPython and import NetworKit.

>>> from networkit import *

Now you should be able to use NetworKit interactively. For usage examples, refer to the UserGuide.

pip install networkit

It is now possible to install Networkit via [sudo] pip[3] install networkit. While this is supposed to be convenient, there are some restrictions. Before you use pip to install NetworKit, Cython, SCons and g++ need to be installed. During installation, the setup will check if the external packages networkit uses are available and print warnings at the end of the installation process. If you don't see any warnings, your system should be ready to use NetworKit.

You can remove NetworKit completely by using the command [sudo] pip[3] uninstall networkit.

IPython Notebook

We recommend that you familiarize yourself with NetworKit through experimenting with the interactive IPython Notebook NetworKit_UserGuide.ipynb located in the folder Doc/Notebooks. To display and work with these notebooks, you have to start a local notebook server from the terminal with:

ipython3 notebook --pylab inline

It can occur, that ipython3 itself works, but the notebook server won't start. In that case, head over to the IPython website and make sure, you have the listed packages installed. If the notebook server starts as it is supposed to, your default browser should open a web interface named "IPython Dashboard". You can either add NetworKit_UserGuide.ipynb from the above mentioned location, or you can point IPython to the location by starting it with

ipython3 notebook --pylab inline --notebook-dir=Doc/Notebooks

The notebook appears in the list and you can start it by clicking on it.

Building the C++ Core only

In case you do not need NetworKit's Python functionality, this section describes how to build the C++ parts only.

We recommend SCons for building the C++ part of NetworKit. Individual settings for your environment will be read from a configuration file. As an example, the file build.conf.example is provided. Copy this to build.conf and edit your environment settings. Then call scons.

The call to SCons has the following options:

scons --optimize=<level> --target=<target>

where <level> can be

  • Dbg debug
  • Opt optimized
  • Pro profiling

and <target> can be

  • Core build NetworKit as a library, required by the Python shell
  • Tests build executable for the unit tests
  • Lib build NetworKit as a library and create symbolic links

For example, to build NetworKit as an optimized library, run scons --optimize=Opt --target=Core

To speed up the compilation on a multicore machine, you can append -jX where X denotes the number of threads to compile with.

Logging is enabled by default. If you want to disable logging functionality, add the following to your scons call:



You actually don't need to build and run our unit tests. However if you experience any issues with NetworKit, you might want to check, if NetworKit runs properly. Please refer to the Unit Tests and Testing section in our DevGuide.

NetworKit as a library

It is also possible to use NetworKit as a library. Therefore, choose the target Lib when compiling NetworKit. The include directives in your C++-application look like the following

#include <NetworKit/graph/Graph.h>

NetworKit in the directory includeis a symlink to the directory networkit/cpp, so the directory structure from the repository is valid. To compile your application, you need to add the paths for the header files and the location of the library. Note, that it is possible to link the different builds (debug, profiling, optimized) of the library. There is a simple source file to demonstrate this. Feel free to compile LibDemo.cpp as follows:

g++ -o LibDemo -std=c++11 -I/path/to/repository/include
    -L/path/to/repository LibDemo.cpp -lNetworKit -fopenmp


We would like to encourage contributions to the NetworKit source code. See the development guide (DevGuide.mdown) for instructions. For support please contact christian.staudt @

Development History

For history and possible future features, refer to the Roadmap.


Responsible Developers

  • Christian L. Staudt - christian.staudt @ - Homepage
  • Henning Meyerhenke - meyerhenke @ - Homepage


  • Maximilian Vogel - maximilian.vogel @


  • Lukas Barth
  • Miriam Beddig
  • Elisabetta Bergamini
  • Stefan Bertsch
  • Pratistha Bhattarai
  • Andreas Bilke
  • Simon Bischof
  • Guido Brückner
  • Patrick Flick
  • Michael Hamann
  • Lukas Hartmann
  • Daniel Hoske
  • Gerd Lindner
  • Moritz v. Looz
  • Yassine Marrakchi
  • Marcel Radermacher
  • Klara Reichard
  • Marvin Ritter
  • Aleksejs Sazonovs
  • Florian Weber
  • Michael Wegner
  • Jörg Weisbarth

External Code

The program source includes: - the The Lean Mean C++ Option Parser by Matthias S. Benkmann. - a Python 3 version of the powerlaw Python module by Jeff Alstott, Ed Bullmore, Dietmar Plenz


The source code of this program is released under the MIT License. We ask you to cite us if you use this code in your project. Feedback is also welcome.


The following is a list of publications on the basis of NetworKit. We ask you to cite the appropriate ones if you found NetworKit useful for your own research.

        Author = {Staudt, Christian L and Sazonovs, Aleksejs and Meyerhenke, Henning},
        Date-Added = {2014-03-13 12:41:29 +0000},
        Date-Modified = {2014-03-13 12:42:47 +0000},
        Journal = {arXiv preprint arXiv:1403.3005},
        Title = {NetworKit: An Interactive Tool Suite for High-Performance Network Analysis},
        Year = {2014}}

      author    = {Christian Staudt and Henning Meyerhenke},
      title     = {Engineering High-Performance Community Detection Heuristics
                   for Massive Graphs},
      journal   = {CoRR},
      volume    = {abs/1304.4453},
      year      = {2013},
      ee        = {},
      bibsource = {DBLP,}}

        Author = {Christian L. Staudt and Henning Meyerhenke},
        Booktitle = {proceedings of the 2013 International Conference on Parallel Processing},
        Date-Added = {2013-10-01 08:13:23 +0000},
        Date-Modified = {2013-10-01 08:13:23 +0000},
        Publisher = {Conference Publishing Services (CPS)},
        Title = {Engineering High-Performance Community Detection Heuristics for Massive Graphs},
        Year = {2013}}