Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

The frequent outbreak of global infectious diseases has prompted the development of rapid
and effective diagnostic tools for the early screening of potential patients in point-of-care …

Accurate remaining useful life (RUL) prediction has gained increasing attention in modern
maintenance management. Considering the data privacy requirements of distributed multi …

Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

Detecting bearing faults helps ensure the healthy operation of machinery and prevents
serious accidents. However, fault diagnosis method based on deep learning relies on the …

State estimation is highly critical for accurately observing the dynamic behavior of the power
grids and minimizing risks from cyber threats. However, existing state estimation methods …

Topological deep learning is a rapidly growing field that pertains to the development of deep
learning models for data supported on topological domains such as simplicial complexes …

Graph neural network is a deep learning approach widely applied on structural and non-
structural scenarios due to its substantial performance and interpretability recently. In a non …