Salīdzināt metodes

	Grafu neironu tīkls	Word2Vec
Nozare	Tīklu analīze	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2017–2018 (major variants)	2013
Autors	—	Tomas Mikolov et al.
Tips	Deep learning on graph-structured data	Neural word-embedding model
Pirmavots	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Citi nosaukumi	GNN, GCN, GAT, GraphSAGE	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Saistītās	5	4
Kopsavilkums	A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.