This survey investigates current techniques for representing qualitative data for use as input
to neural networks. Techniques for using qualitative data in neural networks are well known …

Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

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 …

Message passing neural networks have become a method of choice for learning on graphs,
in particular the prediction of chemical properties and the acceleration of molecular …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

Advancing research in the emerging field of deep graph learning requires new tools to
support tensor computation over graphs. In this paper, we present the design principles and …

Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

Deep neural networks demonstrated their ability to provide remarkable performances on a
wide range of supervised learning tasks (eg, image classification) when trained on extensive …

Artificial intelligence is an advanced method to identify novel anticancer targets and discover
novel drugs from biology networks because the networks can effectively preserve and …

Graph neural network, as a powerful graph representation technique based on deep
learning, has shown superior performance and attracted considerable research interest …