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Home — 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. An introductory talk about NetworKit.

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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. An introductory talk about NetworKit.

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Home — NetworKit | networkit.iti.kit.edu Reviews

https://networkit.iti.kit.edu

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. An introductory talk about NetworKit.

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matteo.rionda.to

Matteo Riondato

http://matteo.rionda.to/soft.php

1 212 775 6641. My current projects are hosted on GitHub. I am also a committer for NetworKit. A toolkit for high-performance network analysis. A LaTeX package to insert notes, todo lists, questions, and answers. It is useful when writing papers with multiple authors. Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages. Centrsampl: If you are looking for the software to approximate betweenness centrality via sampling, check out the implementation in NetworKit.

parco.iti.uka.de

Forschungsgruppe Paralleles Rechnen

http://parco.iti.uka.de/aktuelles-en.shtml

Dec 14, 2015:. Congratulations to our PhD student Elisabetta Bergamini for winning a SIAM Travel Award in order to present our work at ALENEX16! Nov 25, 2015:. Henning Meyerhenke nimmt heute um 19:30 Uhr an einer hochrangig besetzten Podiumsdiskussion. Als Vertreter der Juniorprofessoren/-innen teil. Oct 20, 2015:. Two papers we co-authored on closeness centrality and on hypergraph partitioning have been accepted at ALENEX 2016. Aug 31, 2015:. Jul 22, 2015:. Jul 17, 2015:. Jul 2, 2015:. Jun 30, 2015:.

parco.iti.uka.de

Prof. Dr. Henning Meyerhenke

http://parco.iti.uka.de/henningm

Prof Dr. Henning Meyerhenke. I am the head of the research group Theoretical Computer Science / Parallel Computing. In the Institute of Theoretical Informatics of Karlsruhe Institute of Technology (KIT). Please follow the different categories in the navigation bar on the left to receive more information about my work. In case you are looking for one of my publications, please follow the corresponding link. Do not hesitate to ask me for preprints via e-mail. Hot Topic: NetworKit 4.0. Nov 10, 2015:. Cover ...

parco.iti.kit.edu

Prof. Dr. Henning Meyerhenke

http://parco.iti.kit.edu/henningm

Prof Dr. Henning Meyerhenke. I am the head of the research group Theoretical Computer Science / Parallel Computing. In the Institute of Theoretical Informatics of Karlsruhe Institute of Technology (KIT). Please follow the different categories in the navigation bar on the left to receive more information about my work. In case you are looking for one of my publications, please follow the corresponding link. Do not hesitate to ask me for preprints via e-mail. Hot Topic: NetworKit 4.0. Nov 10, 2015:. Cover ...

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networkit.iti.kit.edu

Home — 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. An introductory talk about NetworKit.

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