There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Despite the promising representation learning of graph neural networks (GNNs), the
supervised training of GNNs notoriously requires large amounts of labeled data from each …

Driven by Moore's law, the chip design complexity is steadily increasing. Electronic Design
Automation (EDA) has been able to cope with the challenging very large-scale integration …

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

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …

Graph Neural Networks (GNNs) have shown great promise in various graph learning tasks.
However, the computational overheads of fitting GNNs to large-scale graphs grow rapidly …

Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-
structured data, most of the current GCNs are shallow due to the notoriously challenging …

While the expressive power and computational capabilities of graph neural networks
(GNNs) have been theoretically studied, their optimization and learning dynamics, in …