Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

48 Zitate (Scopus)

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 a new model Graph Posterior Network (GPN) which explicitly performs Bayesian posterior updates for predictions on interdependent nodes. GPN provably obeys the proposed axioms. (3) We extensively evaluate GPN and a strong set of baselines on semi-supervised node classification including detection of anomalous features, and detection of left-out classes. GPN outperforms existing approaches for uncertainty estimation in the experiments.

OriginalspracheEnglisch
TitelAdvances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021
Redakteure/-innenMarc'Aurelio Ranzato, Alina Beygelzimer, Yann Dauphin, Percy S. Liang, Jenn Wortman Vaughan
Herausgeber (Verlag)Neural information processing systems foundation
Seiten18033-18048
Seitenumfang16
ISBN (elektronisch)9781713845393
PublikationsstatusVeröffentlicht - 2021
Veranstaltung35th Conference on Neural Information Processing Systems, NeurIPS 2021 - Virtual, Online
Dauer: 6 Dez. 202114 Dez. 2021

Publikationsreihe

NameAdvances in Neural Information Processing Systems
Band22
ISSN (Print)1049-5258

Konferenz

Konferenz35th Conference on Neural Information Processing Systems, NeurIPS 2021
OrtVirtual, Online
Zeitraum6/12/2114/12/21

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