9 citations · 11 across the 5 of their papers we have counts for
5 papers
Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning
Yi Luo, Xu Sun, Guangchun Luo +1
Graph neural networks (GNNs) have been widely adopted in engineering applications such as social network analysis, chemical research and computer vision. However, their efficacy is…
Graph Entropy Minimization for Semi-supervised Node Classification
Yi Luo, Guangchun Luo, Ke Qin +1
Node classifiers are required to comprehensively reduce prediction errors, training resources, and inference latency in the industry. However, most graph neural networks (GNN) conc…
Unifying Label-inputted Graph Neural Networks with Deep Equilibrium Models
Yi Luo, Guiduo Duan, Guangchun Luo +1
The success of Graph Neural Networks (GNN) in learning on non-Euclidean data arouses many subtopics, such as Label-inputted GNN (LGNN) and Implicit GNN (IGNN). LGNN, explicitly inp…
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Yi Luo, Aiguo Chen, Ke Yan +1
Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data. Models in this paradigm have to spend extra space to…
Memory-Associated Differential Learning
Yi Luo, Aiguo Chen, Bei Hui +1
Conventional Supervised Learning approaches focus on the mapping from input features to output labels. After training, the learnt models alone are adapted onto testing features to…