Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Efficient Randomized Distributed Coloring in CONGEST (2012.14169v2)

Published 28 Dec 2020 in cs.DS and cs.DC

Abstract: Distributed vertex coloring is one of the classic problems and probably also the most widely studied problems in the area of distributed graph algorithms. We present a new randomized distributed vertex coloring algorithm for the standard CONGEST model, where the network is modeled as an $n$-node graph $G$, and where the nodes of $G$ operate in synchronous communication rounds in which they can exchange $O(\log n)$-bit messages over all the edges of $G$. For graphs with maximum degree $\Delta$, we show that the $(\Delta+1)$-list coloring problem (and therefore also the standard $(\Delta+1)$-coloring problem) can be solved in $O(\log5\log n)$ rounds. Previously such a result was only known for the significantly more powerful LOCAL model, where in each round, neighboring nodes can exchange messages of arbitrary size. The best previous $(\Delta+1)$-coloring algorithm in the CONGEST model had a running time of $O(\log\Delta + \log6\log n)$ rounds. As a function of $n$ alone, the best previous algorithm therefore had a round complexity of $O(\log n)$, which is a bound that can also be achieved by a na\"{i}ve folklore algorithm. For large maximum degree $\Delta$, our algorithm hence is an exponential improvement over the previous state of the art.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Magnús M. Halldórsson (26 papers)
  2. Fabian Kuhn (75 papers)
  3. Yannic Maus (45 papers)
  4. Tigran Tonoyan (19 papers)
Citations (26)