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

被引:133
作者
Staudt, Christian L. [1 ]
Sazonovs, Aleksejs [2 ]
Meyerhenke, Henning [1 ]
机构
[1] Karlsruhe Inst Technol, Inst Theoret Informat, D-76131 Karlsruhe, Germany
[2] Wellcome Trust Sanger Inst, Wellcome Genome Campus, Cambridge CB10 1SA, England
关键词
complex networks; network analysis; network science; parallel graph algorithms; data analysis software;
D O I
10.1017/nws.2016.20
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
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.
引用
收藏
页码:508 / 530
页数:23
相关论文
共 58 条
[1]  
Alahakoon T., 2011, P 4 WORKSHOP SOCIAL, P1, DOI [10.1145/1989656.1989657, DOI 10.1145/1989656.1989657]
[2]   Statistical mechanics of complex networks [J].
Albert, R ;
Barabási, AL .
REVIEWS OF MODERN PHYSICS, 2002, 74 (01) :47-97
[3]  
Bader D., 2014, ENCY SOCIAL NETWORK, P73, DOI DOI 10.1007/978-1-4614-6170-8_23
[4]   Efficient generation of large random networks [J].
Batagelj, V ;
Brandes, U .
PHYSICAL REVIEW E, 2005, 71 (03)
[5]  
Batagelj V, 2004, MATH VIS, P77
[6]   Fast algorithms for determining (generalized) core groups in social networks [J].
Batagelj, Vladimir ;
Zaversnik, Matjaz .
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 2011, 5 (02) :129-145
[7]   Cython: The Best of Both Worlds [J].
Behnel, Stefan ;
Bradshaw, Robert ;
Citro, Craig ;
Dalcin, Lisandro ;
Seljebotn, Dag Sverre ;
Smith, Kurt .
COMPUTING IN SCIENCE & ENGINEERING, 2011, 13 (02) :31-39
[8]  
Bergamini E., 2015, P 17 WORKSH ALG ENG, P133, DOI DOI 10.1137/1.9781611973754.12
[9]   Fully-Dynamic Approximation of Betweenness Centrality [J].
Bergamini, Elisabetta ;
Meyerhenke, Henning .
ALGORITHMS - ESA 2015, 2015, 9294 :155-166
[10]   Fast unfolding of communities in large networks [J].
Blondel, Vincent D. ;
Guillaume, Jean-Loup ;
Lambiotte, Renaud ;
Lefebvre, Etienne .
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2008,