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 …

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

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) emerge as a powerful approach to process non-euclidean
data structures and have been proved powerful in various application domains such as …

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 …

Graph neural networks (GNNs) have been a hot spot of recent research and are widely
utilized in diverse applications. However, with the use of huger data and deeper models, an …

Graph neural networks (GNN) represent an emerging line of deep learning models that
operate on graph structures. It is becoming more and more popular due to its high accuracy …

Graph learning is an emerging technique widely used in diverse applications such as
recommender system and medicine design. Real-world graph learning applications typically …

Building FPGA-based graph learning accelerators is very time-consuming due to the low-
level RTL programming and the complicated design flow of FPGA development. It also …