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 the Readme for a list of existing features and a roadmap of planned features). In this respect, NetworKit is comparable to packages such as NetworkX, albeit with a focus on parallelism and scalability. NetworKit is also a testbed for algorithm engineering and contains a few novel algorithms from recently published research, especially in the area of community detection. As of release 2.0, NetworKit is meant to be built into a Python extension module. High-performance algorithms are written in C++ and exposed to Python via Cython. Python in turn gives us the ability to work interactively and a rich environment of tools for data analysis and scientific computing, such as pandas, matplotlib, numpy and scipy.


Clone the source from and follow instructions in the Readme document. Alternatively, you can download the source as a zip archive - make sure to download the "default" branch.


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2014/03/13 NetworKit 3.1 is an incremental update to our tool suite for high-performance network analysis. Improvements and new features include Eigenvector centrality, PageRank, Betweenness centrality approximation, R-MAT graph generator, BFS/DFS iterators, improved BFS and Dijkstra classes, and improved memory footprint when using large objects on the Python level. More detailed information can be found in the accompanying publication.

2014/03/13 NetworKit 3.0 is the next major release of our open-source tookit for high-performance network analysis. Since the last release in November, NetworKit has received several improvements under the hood as well as an extension of the feature set. What started as a testbed for parallel community detection algorithms has evolved into a diverse set of tools that make it easy to characterize complex networks. This has been successfully scaled to large data sets with up to several billions of edges. More detailed information can be found in the accompanying publication. This being an open-source project, we are very interested in incorporating feedback from data analysts and algorithm engineers. Feel free to contact us with any question on how NetworKit could be applied in your field of research.

2013/11/11 NetworKit 2.0: Second major release of NetworKit. The toolkit has been improved by adding several graph algorithms and an interactive shell based on Python/Cython. We begin a more frequent release cycle.

2013/03/17 NetworKit 1.0: Initial release of the community detection component. With this release, we would like to encourage reproduction of our results, reuse of code and contributions by the community.

2012/10/01 Our toolkit for high-performance network analysis is under active development since October 2012.