Recent Advances in Fully Dynamic Graph Algorithms
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Abstract (arXiv)
In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 3; 2026: 1.
5 papers.
- 2022ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networkscited by 3
- 2025A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimizationcited by 2
- 2025Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulationcited by 0
- 2025Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphscited by 0
- 2026ComNetX: Local Hierarchical Adaptation for Dynamic Community Detectioncited by 0
- 2026ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection10 Jul 2026
- 2025Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs19 May 2025
- 2025Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulation6 May 2025
- 2025A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization23 Mar 2025
- 2022ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks1 Mar 2022