The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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

Deep learning has already revolutionised the way a wide range of data is processed in
many areas of daily life. The ability to learn abstractions and relationships from …

Abstract Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving
graph analytical problems in various real-world applications. Nevertheless, GNNs could …

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there's
an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially …

The growing number of applications of machine learning and data mining in many domains—
from agriculture to business, education, industrial manufacturing, and medicine—gave rise …

Multivariate analysis approaches provide insights into the identification of phenotype
associations in brain connectome data. In recent years, deep learning methods including …

Deep learning models have achieved exceptional predictive performance in a wide variety
of tasks, ranging from computer vision, natural language processing, to graph mining. Many …

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …

As Graph Neural Networks (GNNs) have been widely used in real-world applications, model
explanations are required not only by users but also by legal regulations. However …