The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

In recent years, spectral clustering has become one of the most popular modern clustering
algorithms. It is simple to implement, can be solved efficiently by standard linear algebra …

The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their
expressiveness, contributing to well-known issues such as over-smoothing. To overcome …

Attributes act as intermediate representations that enable parameter sharing between
classes, a must when training data is scarce. We propose to view attribute-based image …

Change detection on multimodal remote sensing images has become an increasingly
interesting and challenging topic in the remote sensing community, which can play an …

Attributes are an intermediate representation, which enables parameter sharing between
classes, a must when training data is scarce. We propose to view attribute-based image …

Artificial Intelligence federates numerous scientific fields in the aim of developing machines
able to assist human operators performing complex treatments---most of which demand high …

In this survey we overview the definitions and methods for graph clustering, that is, finding
sets of “related” vertices in graphs. We review the many definitions for what is a cluster in a …

Methods of dimensionality reduction provide a way to understand and visualize the structure
of complex data sets. Traditional methods like principal component analysis and classical …

This work presents a new perspective on characterizing the similarity between elements of a
database or, more generally, nodes of a weighted and undirected graph. It is based on a …