قارن الطرق

	شبكة الانتباه الرسومية	الشبكات العصبية البيانية (GNNs)
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2018	2017
صاحب الطريقة	Veličković, P. et al.	Kipf, T.N. & Welling, M.
النوع	Graph neural network (attention-based)	Deep learning on graph-structured data
المصدر التأسيسي	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.
الأسماء البديلة	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network
ذات صلة	4	4
الملخص	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).	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.