Signal processing (SP) excels at analyzing, processing, and inferring information defined
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …

Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …

The development of Internet of Things (IoT) technologies has led to the widespread adoption
of monitoring networks for a wide variety of applications, such as smart cities, environmental …

This article provides a new strategy for the heterogeneous change detection (HCD) problem:
solving HCD from the perspective of graph signal processing (GSP). We construct a graph to …

This paper proposes a temporal graph neural network model for forecasting of graph-
structured irregularly observed time series. Our TGNN4I model is designed to handle both …

Federated Learning is widely employed to tackle distributed sensitive data. Existing methods
primarily focus on addressing in-federation data heterogeneity. However, we observed that …

Deep neural networks (DNNs) and especially convolutional neural networks (CNNs) have
revolutionized the way we approach the analysis of large quantities of data. However, the …

Outstanding achievements of graph neural networks for spatiotemporal time series analysis
show that relational constraints introduce an effective inductive bias into neural forecasting …

Entropy metrics (for example, permutation entropy) are nonlinear measures of irregularity in
time series (one-dimensional data). Some of these entropy metrics can be generalised to …

This book is a general introduction to the statistical analysis of networks, and can serve both
as a research monograph and as a textbook. Numerous fundamental tools and concepts …