Salīdzināt metodes

	Pārneses mācīšanās ar grafu neironu tīkliem	Grafu neironu tīkls
Nozare	Dziļā mācīšanās	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2010–2020	2017–2018 (major variants)
Autors	Hu et al. (GNN-specific); Pan & Yang (transfer learning framework)	—
Tips	Transfer learning / graph representation learning	Deep learning on graph-structured data
Pirmavots	Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2020). Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations (ICLR 2020).	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
Citi nosaukumi	TL-GNN, pre-trained GNN, GNN transfer learning, graph transfer learning	GNN, GCN, GAT, GraphSAGE
Saistītās	3	5
Kopsavilkums	Transfer Learning with Graph Neural Networks (GNNs) adapts a GNN pre-trained on a large source graph dataset to a smaller, often label-scarce target graph task. By reusing learned node and edge representations, this approach achieves strong predictive performance where collecting sufficient labeled graph data is expensive or slow — as is common in chemistry, biology, and social network analysis.	A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.