Faster Deterministic Streaming Vertex Coloring
Shiri Chechik, Hongyi Chen, Tianyi Zhang

TL;DR
This paper introduces a new deterministic semi-streaming algorithm for graph vertex coloring that achieves an $O( ext{max degree})$-coloring in sublogarithmic passes, improving over previous methods.
Contribution
It presents the first deterministic streaming algorithm with linear palette size and sublogarithmic pass complexity for vertex coloring.
Findings
Achieves $O( ext{max degree})$-coloring in $O( oot{ ext{log} ext{max degree}})$ passes.
First deterministic algorithm with linear palette size and sublogarithmic passes.
Improves deterministic streaming graph coloring trade-offs significantly.
Abstract
Graph coloring is a fundamental problem in computer science. In the semi-streaming model, an input graph on vertices and maximum degree is presented as a stream of edges, and the goal is to compute a vertex coloring using a small number of colors while storing only bits of memory. Recent work has revealed an exponential separation between randomized and deterministic approaches in this setting: while randomized algorithms can achieve a -coloring in a single pass [Assadi, Chen, and Khanna, 2019], any single-pass deterministic algorithm requires colors [Assadi, Chen, and Sun, 2022]. Consequently, deterministic algorithms that use few colors must necessarily make multiple passes over the stream. Prior to this work, the best known deterministic trade-offs were: an -coloring in 2 passes, an…
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