Algorithms for Large-scale Network Analysis and the NetworKit Toolkit

"Medical Computer Vision: Algorithms for Big Data" : International Workshop, MCV 2015, held in conjunction with MICCAI 2015, Munich, Germany, October 9, 2015 : revised selected papers. MCV (Workshop) (5th : 2015 : Munich, Germany) Pub Date : 2022-09-20 DOI:10.48550/arXiv.2209.13355

Eugenio Angriman, Alexander van der Grinten, M. Hamann, Henning Meyerhenke, M. Penschuck

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引用次数: 2

Abstract

The abundance of massive network data in a plethora of applications makes scalable analysis algorithms and software tools necessary to generate knowledge from such data in reasonable time. Addressing scalability as well as other requirements such as good usability and a rich feature set, the open-source software NetworKit has established itself as a popular tool for large-scale network analysis. This chapter provides a brief overview of the contributions to NetworKit made by the DFG Priority Programme SPP 1736 Algorithms for Big Data. Algorithmic contributions in the areas of centrality computations, community detection, and sparsification are in the focus, but we also mention several other aspects -- such as current software engineering principles of the project and ways to visualize network data within a NetworKit-based workflow.

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大规模网络分析算法和网络工具包

大量应用程序中的大量网络数据使得可扩展的分析算法和软件工具在合理的时间内从这些数据中生成知识是必要的。开源软件NetworKit在解决可伸缩性以及其他需求(如良好的可用性和丰富的特性集)的同时，已成为大规模网络分析的流行工具。本章简要概述了DFG优先级计划SPP 1736大数据算法对NetworKit的贡献。算法在中心计算、社区检测和稀疏化领域的贡献是重点，但我们也提到了其他几个方面——比如项目的当前软件工程原则和在基于network的工作流中可视化网络数据的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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"Medical Computer Vision: Algorithms for Big Data" : International Workshop, MCV 2015, held in conjunction with MICCAI 2015, Munich, Germany, October 9, 2015 : revised selected papers. MCV (Workshop) (5th : 2015 : Munich, Germany)

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