In this paper, we examine the main advances registered in the last ten years in Pattern
Recognition methodologies based on graph matching and related techniques, analyzing …

In this paper, we use the quantum Jensen–Shannon divergence as a means of measuring
the information theoretic dissimilarity of graphs and thus develop a novel graph kernel. In …

In this paper we propose a quantum algorithm to measure the similarity between a pair of
unattributed graphs. We design an experiment where the two graphs are merged by …

Recent neural networks designed to operate on graph-structured data have proven effective
in many domains. These graph neural networks often diffuse information using the spatial …

In this paper, we develop a new family of graph kernels where the graph structure is probed
by means of a discrete-time quantum walk. Given a pair of graphs, we let a quantum walk …

Abstract We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for
points on non-rigid three-dimensional shapes based on the average mixing kernel and …

In this article, a novel R-convolution kernel, named the fast quantum walk kernel (FQWK), is
proposed for unattributed graphs. In FQWK, the similarity of the neighborhood-pair …

Graph kernels are powerful tools to bridge the gap between machine learning and data
encoded as graphs. Most graph kernels are based on the decomposition of graphs into a set …

Quantum discrete-time walkers have since their introduction demonstrated applications in
algorithmics and to model and simulate a wide range of transport phenomena. They have …

A wide variety of kernel-based methods have been developed with great successes in many
fields, but very little research has focused on the reproducing kernel function in Reproducing …