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Network Embedding : Theories, Methods, and Applications / by Cheng Yang, Zhiyuan Liu, Cunchao Tu, Chuan Shi, Maosong Sun

By: Yang, Cheng, autor
Contributor(s): Liu, Zhiyuan, autor | Tu, Cunchao, autor | Shi, Chuan, autor | Sun, Maosong, autor
Material type: materialTypeLabelE-bookSeries: (Synthesis Lectures on Artificial Intelligence and Machine Learning, 1939-4616).Publisher: Cham : Springer International Publishing, 2021Edition: 1st edition 2021.Description: 1 recurso en línea (XXI, 220 páginas).ISBN: 9783031015908.Subject: Aprendizaje automático | Redes neuronales artificiales | Espacios vectorialesOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
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.
Summary: 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.
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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.

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.

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