4 papers
Characterizing and Understanding HGNN Training on GPUs
Dengke Han, Mingyu Yan, Xiaochun Ye +1
Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world…
ADE-HGNN: Accelerating HGNNs through Attention Disparity Exploitation
Dengke Han, Meng Wu, Runzhen Xue +3
Heterogeneous Graph Neural Networks (HGNNs) have recently demonstrated great power in handling heterogeneous graph data, rendering them widely applied in many critical real-world d…
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
Yuxiang Zhang, Xin Liu, Meng Wu +4
Graph Neural Networks (GNNs) have emerged as potent models for graph learning. Distributing the training process across multiple computing nodes is the most promising solution to a…
HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation
Runzhen Xue, Dengke Han, Mingyu Yan +8
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both st…