Graph neural networks (GNNs) can extract features by learning both the representation of
each objects (ie, graph nodes) and the relationship across different objects (ie, the edges …

Graph convolutional neural networks (GCNs) have emerged as a key technology in various
application domains where the input data is relational. A unique property of GCNs is that its …

Deep learning on point clouds has attracted increasing attention in the fields of 3D computer
vision and robotics. In particular, graph-based point-cloud deep neural networks (DNNs) …

Dynamic graph networks are widely used for learning time-evolving graphs, but prior work
on training these networks is inefficient due to communication overhead, long …

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for
analyzing non-euclidean graph data. However, to realize efficient GNN training is …

Existing Graph Neural Network (GNN) training frameworks have been designed to help
developers easily create performant GNN implementations. However, most existing GNN …

Graph convolutional networks (GCNs) have been introduced to effectively process non-
Euclidean graph data. However, GCNs incur large amounts of irregularity in computation …

Dynamic Graph Neural Networks (DGNNs) have been widely applied in various real-life
applications, such as link prediction and pandemic forecast, to capture both static structural …

Complex-valued neural networks have better optimization capabilities, stronger robustness,
and richer characterization capabilities compared with real-valued neural networks, which …

Dynamic Graph Neural Networks (DGNNs) are becoming increasingly popular due to their
effectiveness in analyzing and predicting the evolution of complex interconnected graph …