Fully Dynamic Maximal Independent Set with Polylogarithmic Update Time
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Abstract (arXiv)
We present the first algorithm for maintaining a maximal independent set (MIS) of a fully dynamic graph---which undergoes both edge insertions and deletions---in polylogarithmic time. Our algorithm is randomized and, per update, takes O(log2 Delta · log2 n) expected time. Furthermore, the algorithm can be adjusted to have O(log2 Delta · log4 n) worst-case update-time with high probability. Here, n denotes the number of vertices and Delta is the maximum degree in the graph. The MIS problem in fully dynamic graphs has attracted significant attention after a breakthrough result of Assadi, Onak, Schieber, and Solomon [STOC'18] who presented an algorithm with O(m3/4) update-time (and thus broke the natural Omega(m) barrier) where m denotes the number of edges in the graph. This result was improved in a series of subsequent papers, though, the update-time remained polynomial. In particular, the fastest algorithm prior to our work had widetildeO(min√n, m1/3) update-time [Assadi et al. SODA'19]. Our algorithm maintains the lexicographically first MIS over a random order of the vertices. As a result, the same algorithm also maintains a 3-approximation of correlation clustering. We also show that a simpler variant of our algorithm can be used to maintain a random-order lexicographically first maximal matching in the same update-time.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Sep 2019.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 1.
5 papers.
- 2024Dynamic Correlation Clustering in Sublinear Update Timecited by 2
- 2023Dynamic algorithms for k-center on graphscited by 0
- 2024Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Timecited by 0
- 2025Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphscited by 0
- 2026Inductive Correlation Clustering with Graph Neural Networkscited by 0
- 2026Inductive Correlation Clustering with Graph Neural Networks27 Aug 2026
- 2025Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs19 May 2025
- 2024Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time15 Nov 2024
- 2024Dynamic Correlation Clustering in Sublinear Update Time13 Jun 2024
- 2023Dynamic algorithms for k-center on graphs28 Jul 2023