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Network decomposition is a central tool in distributed graph algorithms. We present two improvements on the state of the art for network decomposition, which thus lead to improvements in the (deterministic and randomized) complexity of several well-studied graph problems. - We provide a deterministic distributed network decomposition algorithm with $O(log^5 n)$ round complexity, using $O(log n)$-bit messages. This improves on the $O(log^7 n)$-round algorithm of Rozhov{n} and Ghaffari [STOC20], which used large messages, and their $O(log^8 n)$-round algorithm with $O(log n)$-bit messages. This directly leads to similar improvements for a wide range of deterministic and randomized distributed algorithms, whose solution relies on network decomposition, including the general distributed derandomization of Ghaffari, Kuhn, and Harris [FOCS18]. - One drawback of the algorithm of Rozhov{n} and Ghaffari, in the $mathsf{CONGEST}$ model, was its dependence on the length of the identifiers. Because of this, for instance, the algorithm could not be used in the shattering framework in the $mathsf{CONGEST}$ model. Thus, the state of the art randomized complexity of several problems in this model remained with an additive $2^{O(sqrt{loglog n})}$ term, which was a clear leftover of the older network decomposition complexity [Panconesi and Srinivasan STOC92]. We present a modified version that remedies this, constructing a decomposition whose quality does not depend on the identifiers, and thus improves the randomized round complexity for various problems.
We present a simple deterministic distributed algorithm that computes a $(Delta+1)$-vertex coloring in $O(log^2 Delta cdot log n)$ rounds. The algorithm can be implemented with $O(log n)$-bit messages. The algorithm can also be extended to the more g
Network decomposition is a central concept in the study of distributed graph algorithms. We present the first polylogarithmic-round deterministic distributed algorithm with small messages that constructs a strong-diameter network decomposition with p
This paper gives a new deterministic algorithm for the dynamic Minimum Spanning Forest (MSF) problem in the EREW PRAM model, where the goal is to maintain a MSF of a weighted graph with $n$ vertices and $m$ edges while supporting edge insertions and
An $(epsilon,phi)$-expander decomposition of a graph $G=(V,E)$ is a clustering of the vertices $V=V_{1}cupcdotscup V_{x}$ such that (1) each cluster $V_{i}$ induces subgraph with conductance at least $phi$, and (2) the number of inter-cluster edges i
Network decompositions, as introduced by Awerbuch, Luby, Goldberg, and Plotkin [FOCS89], are one of the key algorithmic tools in distributed graph algorithms. We present an improved deterministic distributed algorithm for constructing network decompo