Graph representation learning
Hamilton, William L.
Graph representation learning by William L. Hamilton. - New York : Springer, ©2022 - xvii, 141 p. : ill. ; 23 cm.
Included bibilographical refferences.
1. Introduction 2. Background and traditional approaches part I. Node embeddings. 3. Neighborhood reconstruction methods
4. Multi-relational data and knowledge graphs part II. Graph neural networks. 5. The graph neural network model
6. Graph neural networks in practice part III. Generative graph models.
8. Traditional graph generation approaches
9. Deep generative models
978303100460
deep learning
geometric deep learning
node embeddings
006.3 / HAM-G
Graph representation learning by William L. Hamilton. - New York : Springer, ©2022 - xvii, 141 p. : ill. ; 23 cm.
Included bibilographical refferences.
1. Introduction 2. Background and traditional approaches part I. Node embeddings. 3. Neighborhood reconstruction methods
4. Multi-relational data and knowledge graphs part II. Graph neural networks. 5. The graph neural network model
6. Graph neural networks in practice part III. Generative graph models.
8. Traditional graph generation approaches
9. Deep generative models
978303100460
deep learning
geometric deep learning
node embeddings
006.3 / HAM-G