论文标题
大规模网络分析和网络工具包的算法
Algorithms for Large-scale Network Analysis and the NetworKit Toolkit
论文作者
论文摘要
大量应用程序中大量的大量网络数据使可扩展的分析算法和软件工具在合理的时间内从此类数据中生成知识所需。开源软件NetworkIT解决了可扩展性以及其他要求，例如良好的可用性和丰富的功能集，已确立了自己的大规模网络分析工具。本章简要概述了DFG Priority程序对大数据的贡献SPP 1736算法。重点是集中度计算，社区检测和稀疏领域的算法贡献，但我们还提到了其他几个方面 - 例如，项目的当前软件工程原理以及在基于网络的工作流中可视化网络数据的方法。
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.
