Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of develo** new drugs. Deep learning (DL) …

Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Abstract Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using
graph structures based on the relational inductive bias (homophily assumption). While …

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Abstract Graph Neural Networks (GNNs) that are based on the message passing (MP)
paradigm generally exchange information between 1-hop neighbors to build node …

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 …

Researchers and practitioners have attempted to predict the financial market by analyzing
textual (eg, news articles and social media) and numeric data (eg, hourly stock prices, and …

In recent years, the Transformer architecture has proven to be very successful in sequence
processing, but its application to other data structures, such as graphs, has remained limited …

Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

Homophily principle, ie, nodes with the same labels are more likely to be connected, has
been believed to be the main reason for the performance superiority of Graph Neural …