Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model
in broad application fields for their effectiveness in learning over graphs. To scale GNN …

The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

With graphs rapidly growing in size and deeper graph neural networks (GNNs) emerging,
the training and inference of GNNs become increasingly expensive. Existing network weight …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Automatic transistor sizing is a challenging problem in circuit design due to the large design
space, complex performance tradeoffs, and fast technology advancements. Although there …

Deep learning systems have been successfully applied to Euclidean data such as images,
video, and audio. In many applications, however, information and their relationships are …

Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three
years. Compared with other deep learning modalities, high-performance hardware …

Graph convolutional neural networks (GCNs) have emerged as an effective approach to
extend deep learning for graph data analytics. Given that graphs are usually irregular, as …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …