Unifying topological structure and self-attention mechanism for node classification in directed networks
Abstract Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend beyond first-order connections and e...
Saved in:
Main Authors: | Yue Peng, Jiwen Xia, Dafeng Liu, Miao Liu, Long Xiao, Benyun Shi |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-024-84816-z |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Unifying spatiotemporal and frequential attention for traffic prediction
by: Qi Guo, et al.
Published: (2025-01-01) -
Model-enhanced spatial-temporal attention networks for traffic density prediction
by: Qi Guo, et al.
Published: (2024-11-01) -
NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification
by: Shuaiqi Liu, et al.
Published: (2024-01-01) -
Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification
by: Getinet Yilma, et al.
Published: (2025-03-01) -
Classification of the materials, forming part of the unified agglutinant sands
by: D. M. Kukuj, et al.
Published: (2003-11-01)