A local algorithm is a distributed algorithm that runs in constant time, independently of the
size of the network. Being highly scalable and fault tolerant, such algorithms are ideal in the …

In this paper, from a theoretical perspective, we study how powerful graph neural networks
(GNNs) can be for learning approximation algorithms for combinatorial problems. To this …

We present the first super-linear lower bounds for natural graph problems in the CONGEST
model, answering a long-standing open question. Specifically, we show that any exact …

We study the problem of tracking multiple moving targets using a team of mobile robots.
Each robot has a set of motion primitives to choose from in order to collectively maximize the …

This work presents a classification of weak models of distributed computing. We focus on
deterministic distributed algorithms, and we study models of computing that are weaker …

We present a distributed algorithm that finds a maximal edge packing in O (Δ+ log* W)
synchronous communication rounds in a weighted graph, independent of the number of …

This article proves strong lower bounds for distributed computing in the congest model, by
presenting the bit-gadget: a new technique for constructing graphs with small cuts. The …

The Massive Parallel Computation (MPC) model is a theoretical framework for popular
parallel and distributed platforms such as MapReduce, Hadoop, or Spark. We consider the …

We present a simple deterministic distributed (2+ ε)-approximation algorithm for minimum-
weight vertex cover, which completes in O (log Δ/εlog log Δ) rounds, where Δ is the …

Fault tolerance is one of the main concepts in distributed computing. It has been tackled from
different angles, eg by building replicated systems that can survive crash failures of …