Võrdle meetodeid
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| Graafiline närvivõrk× | Word2Vec× | |
|---|---|---|
| Valdkond≠ | Võrgustikuanalüüs | Tekstikaeve |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 2017–2018 (major variants) | 2013 |
| Looja≠ | — | Tomas Mikolov et al. |
| Tüüp≠ | Deep learning on graph-structured data | Neural word-embedding model |
| Algallikas≠ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR). DOI ↗ | Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. link ↗ |
| Rööpnimetused≠ | GNN, GCN, GAT, GraphSAGE | word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri |
| Seotud≠ | 5 | 4 |
| Kokkuvõte≠ | 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. |
| ScholarGateAndmestik ↗ |
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