Quantum random walk is the quantum counterpart of a classical random walk. The classical
random walk concept has long been used as a computational framework for designing …

Recently a new feature representation and data analysis methodology based on a
topological tool called persistent homology (and its persistence diagram summary) has …

Quantifying the differences between networks is a challenging and ever-present problem in
network science. In recent years, a multitude of diverse, ad hoc solutions to this problem …

This paper proposes a new graph convolutional neural network architecture based on a
depth-based representation of graph structure deriving from quantum walks, which we refer …

Abstract Graph Neural Networks (GNNs) and Graph Kernels (GKs) are two fundamental
tools used to analyze graph-structured data. Efforts have been recently made in developing …

Brain functional networks (BFNs) constructed from resting-state functional magnetic
resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of …

The convolution operator at the core of many modern neural architectures can effectively be
seen as performing a dot product between an input matrix and a filter. While this is readily …

The field of quantum computing (QC) is expanding, with efforts being made to apply it to
areas previously covered by classical algorithms and methods. Bioinformatics is one such …

Graph kernels are applied heavily for the classification of structured data. In this paper, we
propose a deep Rényi entropy graph kernel for this purpose. We gauge the deep …

Graph representations offer powerful and intuitive ways to describe data in a multitude of
application domains. Here, we consider stochastic processes generating graphs and …