Search Results for author:
Found 4 papers, 2 papers with code
Graph-based Knowledge Distillation: A survey and experimental evaluation
It then provides a comprehensive summary of three types of Graph-based Knowledge Distillation methods, namely Graph-based Knowledge Distillation for deep neural networks (DKD), Graph-based Knowledge Distillation for GNNs (GKD), and Self-Knowledge Distillation based Graph-based Knowledge Distillation (SKD).
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation
In particular: 1) existing methods do not explicitly encode and capture the evolution of short-term preference as sequential methods do; 2) simply using last few interactions is not enough for modeling the changing trend.
HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks
To the best of our knowledge, we are the first to propose a HIgh-order RElational (HIRE) knowledge distillation framework on heterogeneous graphs, which can significantly boost the prediction performance regardless of model architectures of HGNNs.
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks.
Ranked #22 on
Node Property Prediction
on ogbn-mag
