Dengdi Sun
Biographic Data
| ID | 10054653 |
|---|---|
| NAME | Dengdi Sun |
| GIVEN NAMES | Dengdi |
| FAMILY NAME | Sun |
| SIGNATURE | SUN D |
| AFFILIATIONS | Anhui University |
| ORCID | 0000-0002-0164-7944 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Multiview Graph Contrastive Learning Based on Learnable Graph Augmentation
Graph contrastive learning has recently gained prominence as a key technique in the domain of graph representation learning. Most graph contrastive learning methods generate two graph views by augmenting the input graph and maximizing the consistency of their representations. However, due to a lack of prior knowledge of graph data, existing graph augmentation methods often generate low-quality views, which can destroy the core structural informat…
Tiragnn
Social recommendation systems predict user preferences by using social relationships to address data sparsity and cold-start problems. Since social relations and user–item interactions can naturally be modeled as graph structures, graph neural networks (GNNs) have achieved significant success in social recommendation. However, most existing models fail to incorporate temporal information when modeling user–item interactions and rely solely on exp…
No prominent works on this page.
Tiragnn
Social recommendation systems predict user preferences by using social relationships to address data sparsity and cold-start problems. Since social relations and user–item interactions can naturally be modeled as graph structures, graph neural networks (GNNs) have achieved significant success in social recommendation. However, most existing models fail to incorporate temporal information when modeling user–item interactions and rely solely on exp…
Multiview Graph Contrastive Learning Based on Learnable Graph Augmentation
Graph contrastive learning has recently gained prominence as a key technique in the domain of graph representation learning. Most graph contrastive learning methods generate two graph views by augmenting the input graph and maximizing the consistency of their representations. However, due to a lack of prior knowledge of graph data, existing graph augmentation methods often generate low-quality views, which can destroy the core structural informat…
Advanced Graph Neural Networks (2 works) · Graph (2 works) · Graph theory (2 works) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Cognitive science (1 work) · Computer Science (1 work) · Data mining (1 work) · Data science (1 work) · Domain Adaptation and Few-Shot Learning (1 work)