1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AR2024
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration
Runzhen Xue, Mingyu Yan, Dengke Han +3
Heterogeneous Graph Neural Networks (HGNNs) have expanded graph representation learning to heterogeneous graph fields. Recent studies have demonstrated their superior performance a…
cs.AR2024★ 1 cited
GDR-HGNN: A Heterogeneous Graph Neural Networks Accelerator Frontend with Graph Decoupling and Recoupling
Runzhen Xue, Mingyu Yan, Dengke Han +4
Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability of graph representation learning to heterogeneous graphs. However, the irregular memory access pattern…
cs.LG2022
Rethinking Efficiency and Redundancy in Training Large-scale Graphs
Xin Liu, Xunbin Xiong, Mingyu Yan +4
Large-scale graphs are ubiquitous in real-world scenarios and can be trained by Graph Neural Networks (GNNs) to generate representation for downstream tasks. Given the abundant inf…