3 accepted papers
Graph Neural Networks (GNNs) have effectively improved the performance of Cognitive Diagnosis Models (CDMs). Existing works have proposed a series of Graph-based Cognitive Diagnosis Frameworks (GCDFs) to enhance robustness to noise. However, these robust designs are often general methods for GNNs an
Fair graph neural networks aim to eliminate discriminatory biases in predictions. Existing approaches often rely on adversarial learning to mitigate dependencies between sensitive attributes and labels but face challenges due to optimisation difficulties. A key limitation lies in neglecting intrinsi
Graph unlearning technology has become increasingly important since the advent of the `right to be forgotten' and the growing concerns about the privacy and security of artificial intelligence. Graph unlearning aims to quickly eliminate the effects of specific data on graph neural networks (GNNs). H…