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

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 s...

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Bibliographic Details
Published in:Network science (Cambridge University Press) Vol. 4; no. 4; pp. 508 - 530
Main Authors: STAUDT, CHRISTIAN L., SAZONOVS, ALEKSEJS, MEYERHENKE, HENNING
Format: Journal Article
Language:English
Published: New York, USA Cambridge University Press 01.12.2016
Subjects:
Algorithms
Data analysis
Heuristic
Network analysis
Open source software
Programming languages
Software
Task performance
network analysis
complex networks
data analysis software
network science
parallel graph algorithms
ISSN:2050-1242, 2050-1250
Online Access:Get full text
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Summary: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.
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ISSN:2050-1242
2050-1250
DOI:10.1017/nws.2016.20