Salīdzināt metodes

	Daudzvalodu grafu neironu tīkls	Pārneses mācīšanās ar grafu neironu tīkliem
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019	2010–2020
Autors	Various (Kipf & Welling 2017 for GNN; multilingual extensions from NLP community ~2019)	Hu et al. (GNN-specific); Pan & Yang (transfer learning framework)
Tips	Graph-based deep learning with multilingual node/edge features	Transfer learning / graph representation learning
Pirmavots	Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR 2017.	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).
Citi nosaukumi	Multilingual GNN, cross-lingual GNN, multilingual graph network, multilingual relational GNN	TL-GNN, pre-trained GNN, GNN transfer learning, graph transfer learning
Saistītās	5	3
Kopsavilkums	A Multilingual Graph Neural Network (Multilingual GNN) applies graph-based message-passing over nodes and edges that carry features from two or more languages. It is used for tasks such as cross-lingual entity alignment, multilingual knowledge-graph completion, and relation extraction across parallel or comparable corpora, allowing structural and semantic information from multiple languages to be jointly learned.	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.