Deep learning models on graphs have achieved remarkable performance in various graph
analysis tasks, eg, node classification, link prediction, and graph clustering. However, they …

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 …

We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs
to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical …

A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

Graph neural networks (GNNs) have shown excellent performance in a wide range of
applications such as recommendation, risk control, and drug discovery. With the increase in …

Large-scale graph-structured computation is central to tasks ranging from targeted
advertising to natural language processing and has led to the development of several graph …

Abstract Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art
method for graph-based learning tasks. However, training GCNs at scale is still challenging …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

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 (GNN) have shown great success in learning from graph-structured
data. They are widely used in various applications, such as recommendation, fraud …