Popular graph neural networks implement convolution operations on graphs based on
polynomial spectral filters. In this paper, we propose a novel graph convolutional layer …

The rise of graph-structured data such as social networks, regulatory networks, citation
graphs, and functional brain networks, in combination with resounding success of deep …

Precise battery capacity estimation and monitoring are of extreme importance for the future
intelligent battery management system. The primary technical issues result from the absence …

Dealing with data and observations has always been an important aspect of discovery in
science. The idea that science is related to data was brilliantly summarised by Fourier in his …

In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on
higher-order networks. Drawing analogies from discrete and graph signal processing, we …

Precisely battery state of health estimation and remaining useful lifetime prediction are
crucial factors in ensuring the reliability and safety for system operation. This paper thus …

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

Spectral clustering refers to a family of well-known unsupervised learning algorithms. Rather
than attempting to cluster points in their native domain, one constructs a (usually sparse) …

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

We study the optimal design of graph filters (GFs) to implement arbitrary linear
transformations between graph signals. GFs can be represented by matrix polynomials of …