Σύγκριση μεθόδων

	Δικτυακό Συνελικτικό Νευρωνικό Δίκτυο (GCN)	Δίκτυο Προσοχής Γραφήματος
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2017	2018
Δημιουργός	Kipf, T. N. & Welling, M.	Veličković, P. et al.
Τύπος	Spectral graph neural network (semi-supervised node classification)	Graph neural network (attention-based)
Θεμελιώδης πηγή	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.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
Εναλλακτικές ονομασίες	GCN, graph convolutional network, spectral graph convolution, Kipf-Welling GCN	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Συναφείς	1	4
Σύνοψη	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.	The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).