Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …

Drug repurposing—identifying new therapeutic uses for approved drugs—is often a
serendipitous and opportunistic endeavour to expand the use of drugs for new diseases …

This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …

Measuring similarity of neural networks to understand and improve their behavior has
become an issue of great importance and research interest. In this survey, we provide a …

While instance-level explanation of GNN is a well-studied problem with plenty of
approaches being developed, providing a global explanation for the behaviour of a GNN is …

Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph
neural networks (GNNs) have become one of the most popular methods in recent years …

Following a fast initial breakthrough in graph-based learning, Graph Neural Networks
(GNNs) have reached a widespread application in many science and engineering fields …

Network biology is an interdisciplinary field bridging computational and biological sciences
that has proved pivotal in advancing the understanding of cellular functions and diseases …

The use of sophisticated machine learning (ML) models, such as graph neural networks
(GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly …