Krahasoni metodat

	Rrjeti nervor grafiko me mbikëqyrje të dobët	Rrjeti konvolucional graf (GCN)
Fusha	Mësimi i thellë	Mësimi i thellë
Familja	Machine learning	Machine learning
Viti i origjinës	2017–2019	2017
Krijuesi	Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm	Kipf, T. N. & Welling, M.
Lloji	Graph-based deep learning with imperfect supervision	Spectral graph neural network (semi-supervised node classification)
Burimi themelues	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.
Emërtime të tjera	WS-GNN, graph neural network with weak supervision, noisy-label GNN, partially supervised GNN	GCN, graph convolutional network, spectral graph convolution, Kipf-Welling GCN
Të lidhura	6	1
Përmbledhja	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.