Network Embedding Theories, Methods, and Applications
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions
| Main Authors: | , , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2021, 2021
|
| Edition: | 1st ed. 2021 |
| Series: | Synthesis Lectures on Artificial Intelligence and Machine Learning
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Preface
- Acknowledgments
- The Basics of Network Embedding
- Network Embedding for General Graphs
- Network Embedding for Graphs with Node Attributes
- Revisiting Attributed Network Embedding: A GCN-Based Perspective
- Network Embedding for Graphs with Node Contents
- Network Embedding for Graphs with Node Labels
- Network Embedding for Community-Structured Graphs
- Network Embedding for Large-Scale Graphs
- Network Embedding for Heterogeneous Graphs
- Network Embedding for Social Relation Extraction
- Network Embedding for Recommendation Systems on LBSNs
- Network Embedding for Information Diffusion Prediction
- Future Directions of Network Embedding
- Bibliography
- Authors' Biographies