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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.19527 |
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| _version_ | 1866911078118588416 |
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| author | Wang, Yihan Zhao, Jianing |
| author_facet | Wang, Yihan Zhao, Jianing |
| contents | Graph-structured data are pervasive across domains including social networks, biological networks, and knowledge graphs. Due to their non-Euclidean nature, such data pose significant challenges to conventional machine learning methods. This study investigates graph data structures, classical graph algorithms, and Graph Neural Networks (GNNs), providing comprehensive theoretical analysis and comparative evaluation. Through comparative experiments, we quantitatively assess performance differences between traditional algorithms and GNNs in node classification and clustering tasks. Results show GNNs achieve substantial accuracy improvements of 43% to 70% over traditional methods. We further explore integration strategies between classical algorithms and GNN architectures, providing theoretical guidance for advancing graph representation learning research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19527 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research on the application of graph data structure and graph neural network in node classification/clustering tasks Wang, Yihan Zhao, Jianing Machine Learning Graph-structured data are pervasive across domains including social networks, biological networks, and knowledge graphs. Due to their non-Euclidean nature, such data pose significant challenges to conventional machine learning methods. This study investigates graph data structures, classical graph algorithms, and Graph Neural Networks (GNNs), providing comprehensive theoretical analysis and comparative evaluation. Through comparative experiments, we quantitatively assess performance differences between traditional algorithms and GNNs in node classification and clustering tasks. Results show GNNs achieve substantial accuracy improvements of 43% to 70% over traditional methods. We further explore integration strategies between classical algorithms and GNN architectures, providing theoretical guidance for advancing graph representation learning research. |
| title | Research on the application of graph data structure and graph neural network in node classification/clustering tasks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.19527 |