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NetworKit: A tool suite for large-scale complex network analysis

Published online by Cambridge University Press:  28 December 2016

CHRISTIAN L. STAUDT ,
ALEKSEJS SAZONOVS  and
HENNING MEYERHENKE
CHRISTIAN L. STAUDT
Affiliation:
Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany (e-mail: [email protected])
ALEKSEJS SAZONOVS
Affiliation:
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK (e-mail: [email protected])
HENNING MEYERHENKE
Affiliation:
Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany (e-mail: [email protected])
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Abstract

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We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of connections. We describe the methodology applied to develop scalable solutions to network analysis problems, including techniques like parallelization, heuristics for computationally expensive problems, efficient data structures, and modular software architecture. Our goal for the software is to package results of our algorithm engineering efforts and put them into the hands of domain experts. NetworKit is implemented as a hybrid combining the kernels written in C++ with a Python frontend, enabling integration into the Python ecosystem of tested tools for data analysis and scientific computing. The package provides a wide range of functionality (including common and novel analytics algorithms and graph generators) and does so via a convenient interface. In an experimental comparison with related software, NetworKit shows the best performance on a range of typical analysis tasks.

Keywords

complex networksnetwork analysisnetwork scienceparallel graph algorithmsdata analysis software
Type
Research Article
Information
Network Science , Volume 4 , Issue 4 , December 2016 , pp. 508 - 530
Copyright
Copyright © Cambridge University Press 2016 

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