Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

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

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 …

Implementing precision medicine hinges on the integration of omics data, such as
proteomics, into the clinical decision-making process, but the quantity and diversity of …

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 …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Graphs have been widely used to represent complex data in many applications, such as e-
commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data …

Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …

Designing and generating new data under targeted properties has been attracting various
critical applications such as molecule design, image editing and speech synthesis …