AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks
Graph neural networks (GNNs), particularly graph convolutional networks (GCNs), have demonstrated remarkable success in modeling graph-structured data across diverse applications. A critical yet underexplored aspect of GCN design lies in graph representation normalization, where the choice of normal...
Saved in:
Main Authors: | Chuan Dai, Yajuan Wei, Hao Wang, Ying Liu, Zhijie Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/11059875/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network
by: Huali Zhu, et al.
Published: (2025-01-01) -
EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land
by: Xianju Li, et al.
Published: (2025-01-01) -
Spatial Multifeature and Dual-Layer Multihop Graph Convolution Networks for Hyperspectral Image Classification
by: Xiangyue Yu, et al.
Published: (2025-01-01) -
Visual learning graph convolution for multi-grained orange quality grading
by: Zhi-bin GUAN, et al.
Published: (2023-01-01) -
Hyperspectral Band Selection via Heterogeneous Graph Convolutional Self-Representation Network
by: Junde Chen, et al.
Published: (2025-01-01)