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...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11059875/ |
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| author | Chuan Dai Yajuan Wei Hao Wang Ying Liu Zhijie Xu |
| author_facet | Chuan Dai Yajuan Wei Hao Wang Ying Liu Zhijie Xu |
| author_sort | Chuan Dai |
| collection | DOAJ |
| description | 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 normalization scheme significantly influences model performance on learning tasks. Despite its importance, existing research lacks systematic analysis on determining optimal task-specific normalization strategies. This study proposes AdaptedNorm, a principled framework that establishes connections between normalization schemes and GCN task objectives, while introducing three key contributions: 1) A novel random walk re-normalized transformation (RWRT) technique for graph representation normalization; 2) A task-aligned modeling paradigm demonstrating that symmetric re-normalized transformation (SRT) enhances node classification accuracy, while RWRT achieves superior performance in link prediction; 3) Integration of normalization schemes with GNN sparsification strategies, enabling effective model compression without sacrificing performance. Extensive experiments on benchmark datasets (Cora, Citeseer, and PubMed) confirm the generalizability of the findings. The implementation is publicly available at: <uri>https://www.github.com/ChuanDai/AdaptedNorm</uri>. |
| format | Article |
| id | doaj-art-d9f89752aa824f938b45c18f89909d0a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d9f89752aa824f938b45c18f89909d0a2025-08-20T03:41:44ZengIEEEIEEE Access2169-35362025-01-011311486511487910.1109/ACCESS.2025.358452311059875AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning TasksChuan Dai0https://orcid.org/0009-0008-5082-7141Yajuan Wei1Hao Wang2Ying Liu3Zhijie Xu4https://orcid.org/0000-0002-0524-5926School of Intelligent Science and Engineering, Xi’an Peihua University, Xi’an, ChinaSchool of Cyberspace Security, Xi’an University of Posts and Telecommunications, Xi’an, ChinaSchool of Economics and Management, Xi’an University of Technology, Xi’an, ChinaSchool of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, ChinaSchool of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaGraph 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 normalization scheme significantly influences model performance on learning tasks. Despite its importance, existing research lacks systematic analysis on determining optimal task-specific normalization strategies. This study proposes AdaptedNorm, a principled framework that establishes connections between normalization schemes and GCN task objectives, while introducing three key contributions: 1) A novel random walk re-normalized transformation (RWRT) technique for graph representation normalization; 2) A task-aligned modeling paradigm demonstrating that symmetric re-normalized transformation (SRT) enhances node classification accuracy, while RWRT achieves superior performance in link prediction; 3) Integration of normalization schemes with GNN sparsification strategies, enabling effective model compression without sacrificing performance. Extensive experiments on benchmark datasets (Cora, Citeseer, and PubMed) confirm the generalizability of the findings. The implementation is publicly available at: <uri>https://www.github.com/ChuanDai/AdaptedNorm</uri>.https://ieeexplore.ieee.org/document/11059875/GCNlink predictionnode classificationnormalizationRWRTSRT |
| spellingShingle | Chuan Dai Yajuan Wei Hao Wang Ying Liu Zhijie Xu AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks IEEE Access GCN link prediction node classification normalization RWRT SRT |
| title | AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks |
| title_full | AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks |
| title_fullStr | AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks |
| title_full_unstemmed | AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks |
| title_short | AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks |
| title_sort | adaptednorm an adaptive modeling strategy for graph convolutional network based deep learning tasks |
| topic | GCN link prediction node classification normalization RWRT SRT |
| url | https://ieeexplore.ieee.org/document/11059875/ |
| work_keys_str_mv | AT chuandai adaptednormanadaptivemodelingstrategyforgraphconvolutionalnetworkbaseddeeplearningtasks AT yajuanwei adaptednormanadaptivemodelingstrategyforgraphconvolutionalnetworkbaseddeeplearningtasks AT haowang adaptednormanadaptivemodelingstrategyforgraphconvolutionalnetworkbaseddeeplearningtasks AT yingliu adaptednormanadaptivemodelingstrategyforgraphconvolutionalnetworkbaseddeeplearningtasks AT zhijiexu adaptednormanadaptivemodelingstrategyforgraphconvolutionalnetworkbaseddeeplearningtasks |