Expectation-Complete Graph Representations with Homomorphisms

Welke, Pascal and Thiessen, Maximilian and Jogl, Fabian and Gärtner, Thomas (2023) Expectation-Complete Graph Representations with Homomorphisms. In: 40th International Conference on Machine Learning, 2023-07-23 - 2023-07-29, Hawaii.

Full text not available from this repository.

Abstract

We investigate novel random graph embeddings that can be computed in expected polynomial time and that are able to distinguish all non-isomorphic graphs in expectation. Previous graph embeddings have limited expressiveness and either cannot distinguish all graphs or cannot be computed efficiently for every graph. To be able to approximate arbitrary functions on graphs, we are interested in efficient alternatives that become arbitrarily expressive with increasing resources. Our approach is based on Lovász' characterisation of graph isomorphism through an infinite dimensional vector of homomorphism counts. Our empirical evaluation shows competitive results on several benchmark graph learning tasks.

Item Type:
Contribution to Conference (Paper)
Journal or Publication Title:
40th International Conference on Machine Learning
Additional Information:
DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
ID Code:
228769
Deposited By:
Deposited On:
26 Nov 2025 09:25
Refereed?:
Yes
Published?:
Published
Last Modified:
26 Nov 2025 09:25