In the realm of semi-supervised graph learning, pseudo-labeling is a pivotal strategy to
utilize both labeled and unlabeled nodes for model training. Currently, confidence score is …

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

Graph anomaly detection (GAD) has attracted increasing attention in machine learning and
data mining. Recent works have mainly focused on how to capture richer information to …

Deep neural networks are notoriously miscalibrated, ie, their outputs do not reflect the true
probability of the event we aim to predict. While networks for tabular or image data are …

Miscalibration of deep neural networks (DNNs) can lead to unreliable predictions and hinder
their use in clinical decision-making. This miscalibration is often caused by overconfident …

Confidence calibration in Graph Neural Networks (GNNs) aims to align a model's predicted
confidence with its actual accuracy. Recent studies have indicated that GNNs exhibit an …

Graph neural networks (GNNs) have gained popularity in modeling various complex
networks, eg, social network and webpage network. Despite the promising accuracy, the …

Label Smoothing (LS) is a widely-used training technique that adjusts hard labels towards a
softer distribution, which prevents model being over-confidence and enhances model …

This paper delves into the confidence calibration in prediction when using Graph Neural
Networks (GNNs), which has emerged as a notable challenge in the field. Despite their …

As researchers strive to narrow the gap between machine intelligence and human through
the development of artificial intelligence multimedia technologies, it is imperative that we …