ARO-GNN: Adaptive relation-optimized graph neural networks
Abstract Existing Graph Neural Networks (GNNs) suffer from topological noise and attribute distortion when handling complex topology-attribute interactions. To overcome these limitations, we propose Adaptive Relation-Optimized Graph Neural Networks (ARO-GNN). ARO-GNN utilizes complementary informati...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00198-w |
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| _version_ | 1849225845501067264 |
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| author | Yong Lu Zhengguo Lin |
| author_facet | Yong Lu Zhengguo Lin |
| author_sort | Yong Lu |
| collection | DOAJ |
| description | Abstract Existing Graph Neural Networks (GNNs) suffer from topological noise and attribute distortion when handling complex topology-attribute interactions. To overcome these limitations, we propose Adaptive Relation-Optimized Graph Neural Networks (ARO-GNN). ARO-GNN utilizes complementary information from two different graph perspectives and dynamically adjusts the graph structure. Specifically, ARO-GNN transforms the original graph structure into a dual-graph representation. This representation captures high-order relationships and global topological features, thereby addressing the limitations inherent in traditional neighborhood-based structures. In addition, ARO-GNN employs an adaptive relation optimization mechanism that dynamically adjusts the adjacency relationships in the graph by eliminating unreliable edges while incorporating missing connections. This process mitigates topological noise and attribute distortion during message passing. Experimental results show that ARO-GNN significantly improves performance across multiple public datasets for downstream tasks. |
| format | Article |
| id | doaj-art-7963579ae1fa446e84fe40e315e36acb |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-7963579ae1fa446e84fe40e315e36acb2025-08-24T11:53:51ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137711610.1007/s44443-025-00198-wARO-GNN: Adaptive relation-optimized graph neural networksYong Lu0Zhengguo Lin1School of Information Engineering, Minzu University of ChinaSchool of Information Engineering, Minzu University of ChinaAbstract Existing Graph Neural Networks (GNNs) suffer from topological noise and attribute distortion when handling complex topology-attribute interactions. To overcome these limitations, we propose Adaptive Relation-Optimized Graph Neural Networks (ARO-GNN). ARO-GNN utilizes complementary information from two different graph perspectives and dynamically adjusts the graph structure. Specifically, ARO-GNN transforms the original graph structure into a dual-graph representation. This representation captures high-order relationships and global topological features, thereby addressing the limitations inherent in traditional neighborhood-based structures. In addition, ARO-GNN employs an adaptive relation optimization mechanism that dynamically adjusts the adjacency relationships in the graph by eliminating unreliable edges while incorporating missing connections. This process mitigates topological noise and attribute distortion during message passing. Experimental results show that ARO-GNN significantly improves performance across multiple public datasets for downstream tasks.https://doi.org/10.1007/s44443-025-00198-wGraph neural networksGraph representation learningGraph structure learningSpectral convolution |
| spellingShingle | Yong Lu Zhengguo Lin ARO-GNN: Adaptive relation-optimized graph neural networks Journal of King Saud University: Computer and Information Sciences Graph neural networks Graph representation learning Graph structure learning Spectral convolution |
| title | ARO-GNN: Adaptive relation-optimized graph neural networks |
| title_full | ARO-GNN: Adaptive relation-optimized graph neural networks |
| title_fullStr | ARO-GNN: Adaptive relation-optimized graph neural networks |
| title_full_unstemmed | ARO-GNN: Adaptive relation-optimized graph neural networks |
| title_short | ARO-GNN: Adaptive relation-optimized graph neural networks |
| title_sort | aro gnn adaptive relation optimized graph neural networks |
| topic | Graph neural networks Graph representation learning Graph structure learning Spectral convolution |
| url | https://doi.org/10.1007/s44443-025-00198-w |
| work_keys_str_mv | AT yonglu arognnadaptiverelationoptimizedgraphneuralnetworks AT zhengguolin arognnadaptiverelationoptimizedgraphneuralnetworks |