Road network representation learning: a dual graph-based approach
Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. To leverage the input of a road network across these different applications, it is necessary to learn the representations of the roads in the form of vectors, which is...
Saved in:
Main Authors: | Zhang, Liang, Long, Cheng |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/170906 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Graph neural differential equation networks for improved representation learning and robustness
by: Zhao, Kai
Published: (2025) -
Document graph representation learning
by: ZHANG, Ce
Published: (2023) -
Heterogeneous graph neural network with multi-view representation learning
by: SHAO, Zezhi, et al.
Published: (2023) -
Learning on heterogeneous graphs using high-order relations
by: Lee, See Hian, et al.
Published: (2021) -
Explainable image recognition with graph-based feature extraction
by: Azam, Basim, et al.
Published: (2025)