Conference item icon

Conference item

Expander graph propagation

Abstract:

Deploying graph neural networks (GNNs) on whole-graph classification or regression tasks is known to be challenging: it often requires computing node features that are mindful of both local interactions in their neighbourhood and the global context of the graph structure. GNN architectures that navigate this space need to avoid pathological behaviours, such as bottlenecks and oversquashing, while ideally having linear time and space complexity requirements. In this work, we propose an elegant...

Expand abstract
Publication status:
Accepted
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publication website:
https://proceedings.mlr.press/v198/deac22a.html

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
St Catherine's College
Role:
Author
ORCID:
0000-0001-8264-8086
Publisher:
Journal of Machine Learning Research
Journal:
Proceedings of Machine Learning Research More from this journal
Volume:
198
Pages:
38:1-38:18
Publication date:
2022-12-21
Acceptance date:
2022-11-24
Event title:
LoG 2022
Event location:
Virtual Event
Event website:
http://log2022.logconference.org/
Event start date:
2022-12-09
Event end date:
2022-12-12
ISSN:
2640-3498
Language:
English
Keywords:
Pubs id:
1318084
Local pid:
pubs:1318084
Deposit date:
2023-01-05

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP