# Recent Advances in Fully Dynamic Graph Algorithms

arXiv: https://arxiv.org/abs/2102.11169 · HTML: https://mlsanity.com/paper/2102.11169/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2021.

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

- [ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks](https://mlsanity.com/paper/2203.00653/) (2022)
- [A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization](https://mlsanity.com/paper/2504.03692/) (2025)
- [Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulation](https://mlsanity.com/paper/2505.03955/) (2025)
- [Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs](https://mlsanity.com/paper/2505.13754/) (2025)
- [ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection](https://mlsanity.com/paper/2608.16906/) (2026)

## 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.