Comparar métodos

	Red Neuronal de Grafos Débilmente Supervisada	Red Neuronal Convolucional de Grafos (GCN)
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2017–2019	2017
Autor original	Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm	Kipf, T. N. & Welling, M.
Tipo	Graph-based deep learning with imperfect supervision	Spectral graph neural network (semi-supervised node 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).	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France.
Alias	WS-GNN, graph neural network with weak supervision, noisy-label GNN, partially supervised GNN	GCN, graph convolutional network, spectral graph convolution, Kipf-Welling GCN
Relacionados	6	1
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.	Graph Convolutional Network (GCN) is a foundational deep learning architecture for graph-structured data, introduced by Thomas N. Kipf and Max Welling at ICLR 2017. It extends the convolution operation to irregular graph domains via a first-order spectral approximation, enabling each node to aggregate feature information from its neighbors. The model became the canonical baseline for semi-supervised node classification and sparked the modern graph neural network research agenda.