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 …

Graph matching, which refers to a class of computational problems of finding an optimal
correspondence between the vertices of graphs to minimize (maximize) their node and edge …

This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …

In this paper, we provide a framework based upon diffusion processes for finding meaningful
geometric descriptions of data sets. We show that eigenfunctions of Markov matrices can be …

We investigate Relational Graph Attention Networks, a class of models that extends non-
relational graph attention mechanisms to incorporate relational information, opening up …

Virtualizing and sharing networked resources have become a growing trend that reshapes
the computing and networking architectures. Embedding multiple virtual networks (VNs) on …

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 …

Graph matching is an essential problem in computer vision and machine learning. In this
paper, we introduce a random walk view on the problem and propose a robust graph …

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 …

A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded
image segmentation, which can be interpreted as a traditional random walker on a graph …