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

Abstract Graph Neural Networks (GNNs) resemble the Weisfeiler-Lehman (1-WL) test, which
iteratively update the representation of each node by aggregating information from WL-tree …

Images contain rich information and can induce various emotions in the audience. Image
emotion classification aims to identify the emotion categories that images can evoke. It is …

Brain network, which characterizes the functional and structural interactions of brain regions
with graph theory, has been widely utilized to diagnose brain diseases, such as autism …

The success of kernel algorithms depends on the kernel it uses and hence the development
of kernels for structured data like graphs is an active research field. We designed two graph …

Typical R-convolution graph kernels invoke the kernel functions that decompose graphs into
non-isomorphic substructures and compare them. However, overlooking implicit similarities …

Brain networks, describing the functional or structural interactions of brain with graph theory,
have been widely used for brain imaging analysis. Currently, several network representation …

Graph-structured data arise in many scenarios. A fundamental problem is to quantify the
similarities of graphs for tasks such as classification. R-convolution graph kernels are …

Mild cognitive impairment (MCI) belongs to the prodromal stage of Alzheimer's disease (AD).
Accurate diagnosis of MCI is very important for possibly deferring AD progression. Graph …

Graph kernels are conventional methods for computing graph similarities. However, the
existing R-convolution graph kernels cannot resolve both of the two challenges: 1) …