Graphs are flexible mathematical objects that can represent many entities and knowledge
from different domains, including in the life sciences. Graph neural networks (GNNs) are …

Abstract Graph Neural Networks (GNNs) are powerful machine learning prediction models
on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their …

Despite the widespread use of graph neural networks (GNNs) we lack methods to reliably
quantify their uncertainty. We propose a conformal procedure to equip GNNs with prediction …

Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes
domains are often hampered by unreliable predictions. Although numerous uncertainty …

Graph neural networks (GNNs) have advanced the state of the art in various domains.
Despite their remarkable success, the uncertainty estimation of GNN predictions remains …

Despite the success of the carefully-annotated benchmarks, the effectiveness of existing
graph neural networks (GNNs) can be considerably impaired in practice when the real-world …

Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …

Graph neural networks (GNNs) have exhibited impressive performance in modeling graph
data as exemplified in various applications. Recently, the GNN calibration problem has …

Graph neural networks (GNNs) have achieved great success in dealing with graph-
structured data that are prevalent in the real world. The core of graph neural networks is the …

Current graph neural networks (GNNs) that tackle node classification on graphs tend to only
focus on nodewise scores and are solely evaluated by nodewise metrics. This limits …