Metadata-Version: 2.1
Name: networkit
Version: 10.0
Summary: NetworKit is a toolbox for high-performance network analysis
Home-page: https://networkit.github.io/
Download-URL: https://pypi.python.org/pypi/networkit
Author: Christian L. Staudt, Henning Meyerhenke
Author-email: christian.staudt@kit.edu, meyerhenke@kit.edu
License: MIT
Keywords: graph algorithm,network analysis,social network
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Environment :: Other Environment
Classifier: Framework :: IPython
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization


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. 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.
