Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

This work formalizes the associational task of predicting node attribute evolution in temporal
graphs from the perspective of learning equivariant representations. We show that node …

Previous electroencephalogram (EEG) emotion recognition relies on single-task learning,
which may lead to overfitting and learned emotion features lacking generalization. In this …

Graph neural networks (GNNs) are one of the most popular research topics for deep
learning. GNN methods typically have been designed on top of the graph signal processing …

Abstract Graph Neural Networks (GNNs) have shown advantages in various graph-based
applications. Most existing GNNs assume strong homophily of graph structure and apply …

In deep neural networks, better results can often be obtained by increasing the complexity of
previously developed basic models. However, it is unclear whether there is a way to boost …

Graph drawing techniques have been developed in the last few years with the purpose of
producing esthetically pleasing node-link layouts. Recently, the employment of differentiable …

Over the past decade, deep learning has had a revolutionary impact on a broad range of
fields such as computer vision and image processing, computational photography, medical …

Graph neural networks (GNNs) have been ubiquitous in graph node classification tasks.
Most GNN methods update the node embedding iteratively by aggregating its neighbors' …

In recent years, tasks of machine learning ranging from image processing & audio/video
analysis to natural language understanding have been transformed by deep learning. The …