Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Abstract Graph Neural Networks (GNNs) that are based on the message passing (MP)
paradigm generally exchange information between 1-hop neighbors to build node …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-
structured data. Recently, GCNs and subsequent variants have shown superior performance …

Deep graph neural networks (GNNs) have achieved excellent results on various tasks on
increasingly large graph datasets with millions of nodes and edges. However, memory …

Since the proposal of the graph neural network (GNN) by Gori et al.(2005) and Scarselli et
al.(2008), one of the major problems in training GNNs was their struggle to propagate …

Recent years have witnessed the emerging success of graph neural networks (GNNs) for
modeling structured data. However, most GNNs are designed for homogeneous graphs, in …

Graph neural networks (GNNs) have been widely used in representation learning on graphs
and achieved state-of-the-art performance in tasks such as node classification and link …

Graph Convolutional Networks (GCNs) are powerful models for learning representations of
attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer …