Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

Many recent works in the field of graph machine learning have introduced pooling operators
to reduce the size of graphs. In this article, we present an operational framework to unify this …

Abstract Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural …

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility
of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a …

Designing a convolution for a spherical neural network requires a delicate tradeoff between
efficiency and rotation equivariance. DeepSphere, a method based on a graph …

Single-cell genomics technologies enable multimodal profiling of millions of cells across
temporal and spatial dimensions. Experimental limitations prevent the measurement of all …

Cellular automata (CA) are a class of computational models that exhibit rich dynamics
emerging from the local interaction of cells arranged in a regular lattice. In this work we focus …

This article deals with the problem of statistical and system heterogeneity in a cross-silo
federated learning (FL) framework where there exist a limited number of Consumer Internet …

Abstract Intelligent Energy Networks play an important role in the contemporary landscape
of energy production and distribution, and the large amount of data produced by their smart …

We propose a graph spectrum-based Gaussian process for prediction of signals defined on
nodes of the graph. The model is designed to capture various graph signal structures …