2021
DOI: 10.48550/arxiv.2110.14012
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

Abstract: The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNNs). Nonetheless, uncertainty estimation for non-independent node-level predictions is under-explored. In this work, we explore uncertainty quantification for node classification in three ways: (1) We derive three axioms explicitly characterizing the expected predictive uncertainty behavior in homophilic attributed graphs. (2) We propose … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 50 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?