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Graph representation learning

By: Contributor(s): Material type: TextTextPublication details: New York : Springer, ©2022Description: xvii, 141 p. : ill. ; 23 cmISBN:
  • 9783031004605
Subject(s): DDC classification:
  • 006.3 HAM-G
Contents:
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
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Holdings
Item type Current library Collection Call number Status Notes Date due Barcode Item holds
Books Books IIITD Reference Computer Science and Engineering CB 006.3 HAM-G (Browse shelf(Opens below)) Available DBT Project Grant 012928
Total holds: 0

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

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