Dynamic Graph Coloring: Sequential, Parallel, and Distributed
Mohsen Ghaffari, Jaehyun Koo
TL;DR
This work presents a simple randomized framework for maintaining a $(Δ+1)$-coloring in dynamic graphs that seamlessly adapts to sequential, parallel batch, and distributed models. Central to the approach are random geometric layerings and local recoloring rules, enabling $O(1)$ expected worst-case update time in the sequential setting and near-optimal batch-parallel and distributed performance. It delivers both explicit and implicit coloring variants, with deamortization and batch-processing guarantees that yield polylogarithmic worst-case times under additional constructions. Overall, the results unify and extend prior dynamic coloring work, offering scalable, practical performance for dynamic networks.
Abstract
We present a simple randomized algorithm that can efficiently maintain a $(Δ+1)$ coloring as the graph undergoes edge insertion and deletion updates, where $Δ$ denotes an upper bound on the maximum degree. A key advantage is the algorithm's ability to process many updates simultaneously, which makes it naturally adaptable to the parallel and distributed models. Concretely, it gives a unified framework across the models, leading to the following results: - In the sequential setting, the algorithm processes each update in $O(1)$ expected time, worst-case. This matches and strengthens the results of Henzinger and Peng [TALG 2022] and Bhattacharya et al. [TALG 2022], who achieved an $O(1)$ bound but amortized (in expectation and with high probability, respectively), whose work was an improvement of the $O(\log Δ)$ expected amortized bound of Bhattacharya et al. [SODA'18]. - In the parallel setting, the algorithm processes each (arbitrary size) batch of updates using $O(1)$ work per update in the batch in expectation, and in $\text{poly}(\log n)$ depth with high probability. This is, in a sense, an ideal parallelization of the above results. - In the distributed setting, the algorithm can maintain a coloring of the network graph as (potentially many) edges are added or deleted. The maintained coloring is always proper; it may become partial upon updates, i.e., some nodes may temporarily lose their colors, but quickly converges to a full, proper coloring. Concretely, each insertion and deletion causes at most $O(1)$ nodes to become uncolored, but this is resolved within $O(\log n)$ rounds with high probability (e.g., in the absence of further updates nearby--the precise guarantee is stronger, but technical). Importantly, the algorithm incurs only $O(1)$ expected message complexity and computation per update.