# Algorithms for Large-scale Network Analysis and the NetworKit Toolkit

arXiv: https://arxiv.org/abs/2209.13355 · HTML: https://mlsanity.com/paper/2209.13355/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2022.

## Built on by (most cited first, 5 of 5)

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- [On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study](https://mlsanity.com/paper/2402.07294/) (2024)
- [Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks](https://mlsanity.com/paper/2605.01484/) (2026)
- [Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark](https://mlsanity.com/paper/2606.12581/) (2026)

## Abstract (arXiv)

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.