.png){kind=icon}
.png){kind=icon}
Return
Conditional local feature encoding for graph neural networks
.png){kind=icon}
OA
AI
DOI：10.1016/j.asoc.2026.115221.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Our method can be combined with various graph architectures as a plug-in. • Our method can generally improve the performance of various GNN models and tasks. • A linear transformation is used for generating conditional local feature encoding. • The generated conditional encoding considers the adjacent layer relationship.
Keywords:
Deep learning
Graph neural networks
Graph-structured data analysis
Graph representation learning
Conditional local feature encoding
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
No organization information available