Distribution shifts on graphs--the discrepancies in data distribution between training and
employing a graph machine learning model--are ubiquitous and often unavoidable in real …

Learner modeling is a crucial task in online learning environments, where Cognitive
Diagnosis Models (CDMs) are employed to assess learners' knowledge mastery levels …

Abstract Recent advancements in Graph Neural Networks (GNNs) have spurred an upsurge
of research dedicated to enhancing the explainability of GNNs, particularly in critical …

The remarkable success in neural networks provokes the selective rationalization. It
explains the prediction results by identifying a small subset of the inputs sufficient to support …

Existing methods for graph out-of-distribution (OOD) generalization primarily rely on
empirical studies on synthetic datasets. Such approaches tend to overemphasize the causal …

Graph Neural Networks (GNNs) have demonstrated remarkable performance in graph
classification tasks. However, ensuring the explainability of their predictions remains a …