เปรียบเทียบวิธี

	Graph Convolutional Network (GCN)	Graph Attention Network
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2017	2018
ผู้ริเริ่ม	Kipf, T. N. & Welling, M.	Veličković, P. et al.
ประเภท	Spectral graph neural network (semi-supervised node classification)	Graph neural network (attention-based)
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	GCN, graph convolutional network, spectral graph convolution, Kipf-Welling GCN	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
ที่เกี่ยวข้อง	1	4
สรุป	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).