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

In recent years, graph neural networks (GNNs) have become the de facto tool for performing
machine learning tasks on graphs. Most GNNs belong to the family of message passing …

Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph
kernels focus on local properties of graphs and ignore global structure. In this paper, we …

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 …

In this work, we propose two novel quantum walk kernels, namely the Hierarchical Aligned
Quantum Jensen-Shannon Kernels (HAQJSK), between un-attributed graph structures …

With the rapid emergence of graph representation learning, the construction of new large-
scale datasets is necessary to distinguish model capabilities and accurately assess the …

Most state-of-the-art graph kernels only take local graph properties into account, ie, the
kernel is computed with regard to properties of the neighborhood of vertices or other small …

As a simple representation of interactions among distributed brain regions, brain networks
have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's …

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 …

This paper focuses on graph-level representation learning that aims to represent graphs as
vectors that can be directly utilized in downstream tasks such as graph classification. We …