Publication
Title
A query language perspective on graph learning
Author
Abstract
A key component of graph and relational learning methods is the computation of vector representations of the input graphs or relations. The starting point of this tutorial is that we model this computation as queries, mapping relational objects into the realm of real vector spaces. We then revisit recent works in the machine learning community on the expressive power of graph learning methods from this unifying query language perspective. Here, we consider the expressive power related to the discrimination of inputs and to the approximation power of functions. Finally, we argue that the bridge between graph learning and query languages opens many interesting avenues for further research.
Language
English
Source (book)
PODS '23: Proceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, June 18-21, 2023, Seattle, WA
Publication
New york : Assoc computing machinery , 2023
ISBN
979-84-00-70127-6
DOI
10.1145/3584372.3589936
Volume/pages
(2023) , p. 373-379
ISI
001119129400034
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 01.02.2024
Last edited 08.02.2024
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