We present a simple polylogarithmic-time deterministic distributed algorithm for network
decomposition. This improves on a celebrated 2 O (√ log n)-time algorithm of Panconesi …

The algorithms community has been modeling the underlying key features and constraints of
massively parallel frameworks and using these models to discover new algorithmic …

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 …

Any graph with maximum degree Δ admits a proper vertex coloring with Δ+ 1 colors that can
be found via a simple sequential greedy algorithm in linear time and space. But can one find …

In this paper, we present new randomized algorithms that improve the complexity of the
classic (Δ+ 1)-coloring problem, and its generalization (Δ+ 1)-list-coloring, in three well …

We develop a general deterministic distributed method for locally rounding fractional
solutions of graph problems for which the analysis can be broken down into analyzing pairs …

We present a new approach to randomized distributed graph coloring that is simpler and
more efficient than previous ones. In particular, it allows us to tackle the (deg+ 1)-list-coloring …

We present the first conditional hardness results for massively parallel algorithms for some
central graph problems including (approximating) maximum matching, vertex cover …

This paper addresses the cornerstone family of local problems in distributed computing, and
investigates the curious gap between randomized and deterministic solutions under …

We provide a simple new randomized contraction approach to the global minimum cut
problem for simple undirected graphs. The contractions exploit 2-out edge sampling from …