Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …

Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly develo** branch of learning on structured data. During the past 20 years, the …

As a natural extension of link prediction on graphs, hyperlink prediction aims for the
inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than …

The rapid development of reliable quantum processing units opens up novel computational
opportunities for machine learning. Here, we introduce a procedure for measuring the …

Graph neural networks have recently emerged as a very effective framework for processing
graph-structured data. These models have achieved state-of-the-art performance in many …

The problem of accurately measuring the similarity between graphs is at the core of many
applications in a variety of disciplines. Most existing methods for graph similarity focus either …

While highly influential in deep learning, especially in natural language processing, the
Transformer model has not exhibited competitive performance in unsupervised graph …

Identifying the relationship between two articles, eg, whether two articles published from
different sources describe the same breaking news, is critical to many document …

With the development of graph convolutional networks (GCN), deep learning methods have
started to be used on graph data. In additional to convolutional layers, pooling layers are …