Algorithms for Large-scale Network Analysis and the NetworKit Toolkit
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2022.
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Citing papers per year: 2024: 3; 2025: 0; 2026: 2.
5 papers.
- 2024Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluationcited by 7
- 2024Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classificationcited by 7
- 2024On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Studycited by 1
- 2026Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walkscited by 0
- 2026Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmarkcited by 0
- 2026Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark10 Jun 2026
- 2026Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks2 May 2026
- 2024Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification16 Jul 2024
- 2024Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation29 May 2024
- 2024On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study11 Feb 2024