Comparar métodos

	Red Neuronal de Grafos Débilmente Supervisada	Propagación de Etiquetas
Campo	Aprendizaje profundo	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2017–2019	2002
Autor original	Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm	Zhu, X. & Ghahramani, Z.
Tipo	Graph-based deep learning with imperfect supervision	Graph-based semi-supervised classification
Fuente seminal	Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017).	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Alias	WS-GNN, graph neural network with weak supervision, noisy-label GNN, partially supervised GNN	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Relacionados	6	3
Resumen	A Weakly Supervised Graph Neural Network (WS-GNN) is a graph deep-learning approach that learns from graph-structured data — nodes, edges, and their attributes — when only noisy, partial, or indirectly obtained labels are available. By coupling GNN message passing with noise-robust training strategies, it extends graph learning to real-world settings where clean, fully annotated graphs are scarce or expensive to obtain.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.