Bandingkan metode

	Jaringan Saraf Graf Berpengawasan Lemah	Jaringan Saraf Graf Semi-Terawasi
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2017–2019	2017 (GCN formulation); 2004 (label propagation roots)
Pencetus	Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm	Kipf, T. N. & Welling, M. (canonical formulation); Zhou et al. (label propagation precursor)
Tipe	Graph-based deep learning with imperfect supervision	Semi-supervised graph representation learning
Sumber perintis	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. International Conference on Learning Representations (ICLR 2017).
Alias	WS-GNN, graph neural network with weak supervision, noisy-label GNN, partially supervised GNN	Semi-supervised GNN, GNN semi-supervised learning, graph-based semi-supervised classification, semi-supervised node classification
Terkait	6	4
Ringkasan	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.	A semi-supervised graph neural network trains a GNN on a graph where only a small fraction of nodes carry labels, using neighborhood message-passing to spread information from labeled nodes to unlabeled ones. The approach, popularised by Kipf and Welling's 2017 Graph Convolutional Network, achieves strong node-classification accuracy even when labeled examples are scarce.