Graph neural networks (GNNs) have recently become the standard approach for learning
with graph-structured data. Prior work has shed light into their potential, but also their …

Recently, many works studied the expressive power of graph neural networks (GNNs) by
linking it to the $1 $-dimensional Weisfeiler-Leman algorithm ($1\text {-}\mathsf {WL} $) …

Abstract While Graph Neural Networks (GNNs) recently became powerful tools in graph
learning tasks, considerable efforts have been spent on improving GNNs' structural …

Subgraph GNNs are provably expressive neural architectures that learn graph
representations from sets of subgraphs. Unfortunately, their applicability is hampered by the …

This paper studies the relation between Graph Neural Networks and Distributed Computing
Models to propose a new framework for Learning in Graphs. Current Graph Neural Networks …

The Weisfeiler-Leman algorithm ($1 $-WL) is a well-studied heuristic for the graph
isomorphism problem. Recently, the algorithm has played a prominent role in understanding …

We revisit a simple idea for machine learning on graphs, where a random walk on a graph
produces a machine-readable record, and this record is processed by a deep neural …

Graph learning tasks require models to comprehend essential substructure patterns relevant
to downstream tasks, such as triadic closures in social networks and benzene rings in …

Large Language Models (LLMs) have demonstrated remarkable potential in scientific
domains, yet a fundamental question remains unanswered: Can we simulate human …

Leader election is one of the fundamental and well-studied problems in distributed
computing. In this paper, we initiate the study of leader election using mobile agents …