Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML)
algorithms are widely used for solving critical problems in bioinformatics, biomedical …

Vulnerability detection is crucial for ensuring the security and reliability of software systems.
Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding …

Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

The lack of explainability in using relevant clinical knowledge hinders the adoption of
artificial intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevant …

Recently, many post hoc graph neural network (GNN) explanation methods have been
explored to uncover GNNs' predictive behaviors by analyzing the embeddings produced by …

Generating explanations for graph neural networks (GNNs) has been studied to understand
their behaviors in analytical tasks such as graph classification. Existing approaches aim to …

Nowadays, research into personalization has been focusing on explainability and fairness.
Several approaches proposed in recent works are able to explain individual …

Abstract Graph Neural Networks (GNNs) have demonstrated their efficacy in representing
graph-structured data, but their lack of explainability hinders their applicability to critical …