Recent studies show that the predictive performance of graph neural networks (GNNs) is
inconsistent and varies across different experimental runs, even with identical parameters …

Understanding generalization and robustness of machine learning models fundamentally
relies on assuming an appropriate metric on the data space. Identifying such a metric is …

With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG)
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …

We study spectral graph convolutional neural networks (GCNNs), where filters are defined
as continuous functions of the graph shift operator (GSO) through functional calculus. A …

Graph-structured data arise in a variety of real-world context ranging from sensor and
transportation to biological and social networks. As a ubiquitous tool to process graph …

Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and
pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably …

Devising and analysing learning models for spatiotemporal network data is of importance for
tasks including forecasting, anomaly detection, and multi-agent coordination, among others …

When facing graph signal processing tasks, it is typically assumed that the graph describing
the support of the signals is known. However, in many relevant applications the available …

Power system state estimation is a primary and major method for monitoring power grids in
real time. Massive synchrophasor data contains temporal correlations and spatial …

Data augmentation is a series of techniques that generate high-quality artificial data by
manipulating existing data samples. By leveraging data augmentation techniques, AI …