We study a new paradigm of knowledge transfer that aims at encoding graph topological
information into graph neural networks (GNNs) by distilling knowledge from a teacher GNN …

Learning an ideal metric is crucial to many tasks in computer vision. Diverse feature
representations may combat this problem from different aspects; as visual data objects …

Graph embedding is an effective method for deriving low-dimensional representations of
graph data. The power of graph deep learning methods to characterize …

Offline signature verification is a challenging pattern recognition task where a writer model is
inferred using only a small number of genuine signatures. A combination of complementary …

Band selection is an essential step toward effective and efficient hyperspectral image
classification. Traditional supervised band selection methods are often hindered by the …

The aim of this paper is to explore the use of backtrackless walks and prime cycles for
characterizing both labeled and unlabeled graphs. The reason for using backtrackless walks …

This paper investigates graph spectral approaches to the problem of point pattern matching.
Specifically, we concentrate on the issue of how to effectively use graph spectral properties …

In the presentation of the graph edit distance in 1983 and other newer bibliography, authors
state that it is necessary to apply the distance restrictions (non-negativity, identity of …

Despite many structural and functional aspects of the brain organization have been
extensively studied in neuroscience, we are still far from a clear understanding of the …

In geometry processing and shape analysis, several applications have been addressed
through the properties of the spectral kernels and distances, such as commute‐time …