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.

翻译：大量应用中的大量网络数据丰富,使得分析算法和软件工具在合理时间内从这些数据中获取知识十分必要。 解决可扩展性以及其他要求,如良好的可用性和丰富的特性集,开放源软件NetwororKit已经确立自己为大规模网络分析的流行工具。本章简要概述了DFG优先方案SPP 1736 Agorithms对NetworKit的贡献。核心计算、社区探测和聚变等领域的演算法和软件贡献是重点,但我们也提到了其他一些方面 -- -- 例如目前该项目的软件工程原则和如何在基于NetworKit的工作流程中将网络数据直观化。