Methoden vergleichen

	Schwaches Graph-Neuronales Netz (Weakly Supervised Graph Neural Network, WS-GNN)	Label Propagation
Fachgebiet	Deep Learning	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2017–2019	2002
Urheber	Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm	Zhu, X. & Ghahramani, Z.
Typ	Graph-based deep learning with imperfect supervision	Graph-based semi-supervised classification
Wegweisende Quelle	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.
Aliasnamen	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
Verwandt	6	3
Zusammenfassung	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.