Graph-related applications have experienced significant growth in academ...
Heterogeneous graph neural networks (HGNNs) have emerged as powerful
alg...
Graph neural networks (GNNs) have been demonstrated to be a powerful
alg...
Large-scale graphs are ubiquitous in real-world scenarios and can be tra...
Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in...
Limited by the memory capacity and compute power, singe-node graph
convo...
Heterogeneous graph neural networks (HGNNs) deliver the powerful capabil...
Graph neural network (GNN) has been demonstrated to be a powerful model ...
Previous graph analytics accelerators have achieved great improvement on...
Graph neural networks (GNNs) have been a hot spot of recent research and...
Sampling is a critical operation in the training of Graph Neural Network...
Graph Convolutional Networks (GCNs) have received significant attention ...
Graph convolutional network (GCN) emerges as a promising direction to le...
Graph convolutional neural networks (GCNs) have achieved state-of-the-ar...
In this work, we first characterize the hybrid execution patterns of GCN...