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 …

In recent years, there has been growing interest in functional devices based on two-
dimensional (2D) materials, which possess exotic physical properties. With an ultrathin …

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

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

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

Graph convolutional networks (GCNs) have received significant attention from various
research fields due to the excellent performance in learning graph representations. Although …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning
model. However, it can be notoriously challenging to inference GCNs over large graph …

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 …