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We give an improved randomized CONGEST algorithm for distance-$2$ coloring that uses $Delta^2+1$ colors and runs in $O(log n)$ rounds, improving the recent $O(log Delta cdot log n)$-round algorithm in [Halldorsson, Kuhn, Maus; PODC 20]. We then improve the time complexity to $O(log Delta) + 2^{O(sqrt{loglog n})}$.
Imagine a large graph that is being processed by a cluster of computers, e.g., described by the $k$-machine model or the Massively Parallel Computation Model. The graph, however, is not static; instead it is receiving a constant stream of updates. Ho
In this paper we study fractional coloring from the angle of distributed computing. Fractional coloring is the linear relaxation of the classical notion of coloring, and has many applications, in particular in scheduling. It was proved by Hasemann, H
We show that the $(degree+1)$-list coloring problem can be solved deterministically in $O(D cdot log n cdotlog^2Delta)$ rounds in the CONGEST model, where $D$ is the diameter of the graph, $n$ the number of nodes, and $Delta$ the maximum degree. Usin
We give efficient randomized and deterministic distributed algorithms for computing a distance-$2$ vertex coloring of a graph $G$ in the CONGEST model. In particular, if $Delta$ is the maximum degree of $G$, we show that there is a randomized CONGEST
We study the problem of randomized information dissemination in networks. We compare the now standard PUSH-PULL protocol, with agent-based alternatives where information is disseminated by a collection of agents performing independent random walks. I