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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2403.13849 |
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| _version_ | 1866909144486772736 |
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| author | Kovács, László Jlidi, Ali |
| author_facet | Kovács, László Jlidi, Ali |
| contents | One of the hot topics in machine learning is the field of GNN. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. This paper represents a survey, providing a comprehensive overview of Graph Neural Networks (GNNs). We discuss the applications of graph neural networks across various domains. Finally, we present an advanced field in GNNs: graph generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13849 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graphs Unveiled: Graph Neural Networks and Graph Generation Kovács, László Jlidi, Ali Machine Learning Artificial Intelligence One of the hot topics in machine learning is the field of GNN. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. This paper represents a survey, providing a comprehensive overview of Graph Neural Networks (GNNs). We discuss the applications of graph neural networks across various domains. Finally, we present an advanced field in GNNs: graph generation. |
| title | Graphs Unveiled: Graph Neural Networks and Graph Generation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.13849 |