This paper studies the expressive power of graph neural networks falling within the
message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown …

Communication complexity is the mathematical study of scenarios where several parties
need to communicate to achieve a common goal, a situation that naturally appears during …

We present O (loglog n)-round algorithms in the Massively Parallel Computation (MPC)
model, with Õ (n) memory per machine, that compute a maximal independent set, a 1+ ε …

In this work, we use algebraic methods for studying distance computation and subgraph
detection tasks in the congested clique model. Specifically, we adapt parallel matrix …

We present a new technique for efficiently removing almost all short cycles in a graph
without unintentionally removing its triangles. Consequently, triangle finding problems do …

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 present a distributed randomized algorithm finding Minimum Spanning Tree (MST) of a
given graph in O (1) rounds, with high probability, in the congested clique model. The input …

The goal of this article is to identify fundamental limitations on how efficiently algorithms
implemented on platforms such as MapReduce and Hadoop can compute the central …

We study two fundamental graph problems, Graph Connectivity (GC) and Minimum
Spanning Tree (MST), in the well-studied Congested Clique model, and present several …

We present a randomized algorithm that computes a Minimum Spanning Tree (MST) in O
(log* n) rounds, with high probability, in the Congested Clique model of distributed …