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 …

Graph neural networks (GNNs) are powerful machine learning models for various graph
learning tasks. Recently, the limitations of the expressive power of various GNN models …

Recently, it became apparent that a large number of the most interesting structures and
phenomena of the world can be described by networks. To develop a mathematical theory of …

By integrating wireless, video, speech and real-time data access technologies, a unique
shop** assistant service can be created that personalizes the attention provided to a …

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 …

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 …

The question of what can be computed, and how efficiently, is at the core of computer
science. Not surprisingly, in distributed systems and networking research, an equally …

There is a rapidly growing need for scalable algorithms that solve classical graph problems,
such as maximum matching and minimum vertex cover, on massive graphs. For massive …

Locally finding a solution to symmetry-breaking tasks such as vertex-coloring, edge-coloring,
maximal matching, maximal independent set, etc., is a long-standing challenge in distributed …

We consider the problem of estimating the number of triangles in a graph. This problem has
been extensively studied in both theory and practice, but all existing algorithms read the …