text: We study distributed algorithms that find a maximal matching in an anonymous, edge-coloured graph. If the edges are properly coloured with k colours, there is a trivial greedy algorithm that finds a maximal matching in k-1 synchronous communication rounds. The present work shows that the greedy algorithm is optimal in the general case: if A is a deterministic distributed algorithm that finds a maximal matching in anonymous, k-edge-coloured graphs, then there is a worst-case input in which the running time of A is at least k1 rounds. If we focus on graphs of maximum degree Δ, it is known that a maximal matching can be found in O(Δ+ log* k) rounds, and prior work implies a lower bound of Ω(polylog(Δ) + log* k) rounds. Our work closes the gap between upper and lower bounds: the complexity is Θ(Δ+ log* k) rounds. To our knowledge, this is the first linear-in-Δ lower bound for the distributed complexity of a classical graph problem.
  @cite_3: Coloring the nodes of a graph with a small number of colors is one of the most fundamental problems in theoretical computer science. In this paper, we study graph coloring in a distributed setting. Processors of a distributed system are nodes of an undirected graph G. There is an edge between two nodes whenever the corresponding processors can directly communicate with each other. We assume that distributed coloring algorithms start with an initial m-coloring of G. In the paper, we prove new strong lower bounds for two special kinds of coloring algorithms. For algorithms which run for a single communication round---i.e., every node of the network can only send its initial color to all its neighbors---, we show that the number of colors of the computed coloring has to be at least Ω(Δ2 log2 Δ+ log log m). If such one-round algorithms are iteratively applied to reduce the number of colors step-by-step, we prove a time lower bound of Ω(Δ log2 Δ+ log*m) to obtain an O(Δ)-coloring. The best previous lower bounds for the two types of algorithms are Ω(log log m) and Ω(log*m), respectively.
summary: It is easy to come up with an artificial problem with the complexity of @math , but so far no such tight results are known for classical graph problems such as maximal matchings. The lower-bound result by Kuhn and Wattenhofer @cite_3 comes close, but it only applies to a restricted family of algorithms.