Graph Neural Network Training

Language: English

Published by Springer Nature B.V. Jul 2026, 2026

9819557941 / 9789819557943

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This item is printed on demand - Print on Demand Titel. Neuware -Graph Neural Networks (GNNs) have revolutionized the way we learn representations from graph-structured data, becoming a cornerstone for applications in social networks, recommendation systems, biology, and beyond. However, mainstream GNNs rely heavily on message passing, an iterative process of propagating information between connected nodes. While powerful, this method often incurs significant computational costs, making efficient training a growing challenge as graph sizes scale up.This book addresses these challenges by offering a comprehensive exploration of efficient GNN training through the lens of data management. It highlights how innovative techniques, rooted in decades of graph processing research, can optimize the entire training process without compromising performance. By focusing on system-level enhancements and practical solutions, it provides actionable strategies to overcome efficiency bottlenecks in large-scale GNN training.Readers will gain a deeper understanding of the graph data lifecycle in GNN training, with examples that demonstrate how data management techniques can significantly enhance scalability and performance. The book is designed for a broad audience, including students, researchers, and professionals, offering clear explanations and practical insights for anyone looking to master efficient GNN training.Springer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, Heidelberg 206 pp. Englisch.

Seller Inventory # 9789819557943

Title
Graph Neural Network Training
Author
Lei Chen
Publisher
Springer Nature B.V. Jul 2026
Publication year
2026
Condition
Neu
Binding
Buch
Language
English
ISBN 10
9819557941
ISBN 13
9789819557943
Item weight
475 grams
Dimensions
241x160x17 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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