Kovács, László and Jlidi, Ali (2023) Graphs Unveiled: Graph Neural Networks and Graph Generation. PRODUCTION SYSTEMS AND INFORMATION ENGINEERING, 2023 (1). pp. 62-76. ISSN 1785-1270
|
Text
2793-Article Text-16777-1-10-20250816.pdf - Published Version Download (714kB) | Preview |
Official URL: https://doi.org/10.32968/psaie.2023.1.5
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GNN, Graph generation |
| Subjects: | Q Science / természettudomány > QA Mathematics / matematika T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában |
| Depositing User: | Anita Agárdi |
| Date Deposited: | 21 Nov 2025 18:16 |
| Last Modified: | 21 Nov 2025 18:16 |
| URI: | https://real.mtak.hu/id/eprint/229611 |
Actions (login required)
![]() |
Edit Item |




