2021年10月26日 · 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 …

Our main Graph Posterior Network model can be found in gpn.models.gpn_base.py. Ablated models can be found in a similar fashion, i.e. PostNet in gpn.models.gpn_postnet.py, …

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 …

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). …

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 …

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 (GNN). …

2021年9月28日 · The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like abel Propagation (LP) or in Graph Neural …

The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like abel Propagation (LP) or in Graph Neural Networks (GNNs). …

2021年10月26日 · A new model Graph Posterior Network (GPN) is proposed which explicitly performs Bayesian posterior updates for predictions on interdependent nodes and outperforms …

In this work, we make the first attempt to endow a well-trained GNN with the OOD detection ability without modifying its param-eters. To this end, we design a post-hoc framework with Adaptive …