2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion
Dongxiao He, Ruqiong Zhang, Zhizhi Yu +4
Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications. Recently, Graph Neural Network…
cs.LG2023
Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module
Jingbo Zhou, Yixuan Du, Ruqiong Zhang +7
Graph Neural Networks (GNNs), a type of neural network that can learn from graph-structured data through neighborhood information aggregation, have shown superior performance in va…
cs.LG2023★ 2 cited
Adaptive Depth Graph Attention Networks
Jingbo Zhou, Yixuan Du, Ruqiong Zhang +1
As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely…