Graph neural networks (GNNs) are information processing architectures for signals
supported on graphs. They are presented here as generalizations of convolutional neural …

Graph convolutional networks have been successful in addressing graph-based tasks such
as semi-supervised node classification. Existing methods use a network structure defined by …

Because of the curse of dimensionality, the data in high-dimensional space hardly afford
sufficient information for neural networks training. Hence, this is a tough task to approximate …

Graph neural networks (GNNs) have become a staple in problems addressing learning and
analysis of data defined over graphs. However, several results suggest an inherent difficulty …

Network data can be conveniently modeled as a graph signal, where data values are
assigned to the nodes of a graph describing the underlying network topology. Successful …

Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As
a tool used to express relationships between objects, graphs can be deployed to various …

Graph Neural Networks (GNNs) are information processing architectures for signals
supported on graphs. They are presented here as generalizations of convolutional neural …

Graph neural networks have become a staple in problems addressing learning and analysis
of data defined over graphs. However, several results suggest an inherent difficulty in …

It is very common to face classification problems where the number of available labeled
samples is small compared to their dimension. These conditions are likely to cause …

Рассматривается один из способов классификации графовых нейронных сетей на
основе базовых концепций, исследуются основы построения сверточных графовых …