3 papers
cs.LO2026
How Expressive Are Graph Neural Networks in the Presence of Node Identifiers?
Arie Soeteman, Michael Benedikt, Martin Grohe +1
Graph neural networks (GNNs) are a widely used class of machine learning models for graph-structured data, based on local aggregation over neighbors. GNNs have close connections to…
cs.LO2026
Query Languages for Machine-Learning Models
Martin Grohe
In this paper, I discuss two logics for weighted finite structures: first-order logic with summation (FO(SUM)) and its recursive extension IFP(SUM). These logics originate from fou…
cs.DM2024
Some Thoughts on Graph Similarity
Martin Grohe
We give an overview of different approaches to measuring the similarity of, or the distance between, two graphs, highlighting connections between these approaches. We also discuss…