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

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 …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

In the real world all events are connected. There is a hidden network of dependencies that
governs behavior of natural processes. Without much argument it can be said that, of all the …

The task of learning an expressive molecular representation is central to developing
quantitative structure–activity and property relationships. Traditional approaches rely on …

Most graph kernels are an instance of the class of R-Convolution kernels, which measure
the similarity of objects by comparing their substructures. Despite their empirical success …

In this article, we propose a family of efficient kernels for large graphs with discrete node
labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler …

The success of kernel methods has initiated the design of novel positive semidefinite
functions, in particular for structured data. A leading design paradigm for this is the …

We propose graph kernels based on subgraph matchings, ie structure-preserving bijections
between subgraphs. While recently proposed kernels based on common subgraphs (Wale …

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