قارن الطرق

	شبكة عصبية ضعيفة الإشراف على الرسوم البيانية	الشبكة العصبية البيانية شبه المُشرف عليها
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2017–2019	2017 (GCN formulation); 2004 (label propagation roots)
صاحب الطريقة	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)
النوع	Graph-based deep learning with imperfect supervision	Semi-supervised graph representation learning
المصدر التأسيسي	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).
الأسماء البديلة	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
ذات صلة	6	4
الملخص	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.