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
-
Rumor detection using dual embeddings and text-based graph convolutional network
by: Barsha Pattanaik, et al.
Published: (2024-11-01) -
GP-GCN: Global features of orthogonal projection and local dependency fused graph convolutional networks for aspect-level sentiment classification
by: Subo Wei, et al.
Published: (2022-12-01) -
Inverse link prediction with graph convolutional networks for knowledge-preserving sparsification in cheminformatics
by: Elnaz Bangian Tabrizi, et al.
Published: (2025-07-01) -
PLGNN: graph neural networks via adaptive feature perturbation and high-way links
by: Meixia He, et al.
Published: (2025-05-01) -
A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network
by: Huali Zhu, et al.
Published: (2025-01-01)