NetworKit – an interactive tool suite for high-performance network analysis.
NetworKit is an open-source software package for high-performance analysis of large complex networks. Complex networks are equally attractive and challenging targets for data mining, and novel algorithmic solutions, including parallelization, are required to handle data sets containing billions of connections. Our goal for NetworKit is to package results of our algorithm engineering efforts and put them into the hands of domain experts. NetworKit is a hybrid combining the performance of kernels written in C++ with a convenient Python frontend. The package targets shared-memory platforms with OpenMP support. The current feature set includes various analytics kernels such as connected components, diameter, clustering coefficients, community detection, k-core decomposition, degree assortativity and multiple centrality indices, as well as a collection of graph generators. Scaling to massive networks is enabled by techniques such as parallel and sampling-based approximation algorithms. NetworKit is geared towards large networks and satisfies three important criteria: High performance, interactive workflows and integration into the Python ecosystem of tools for data analysis and scientific computation.
Usage examples can be found on http://nbviewer.ipython.org/urls/networkit.github.io/data/uploads/docs/NetworKit_UserGuide.ipynb