Scaling Laws of Network Value: From Communication to Learning - Hardcover

Wang, Cheng; Lin, Yuhang

Hardcover

ISBN 10: 9819581095 ISBN 13: 9789819581092

Publisher: Springer, 2026

Synopsis

This book bridges two seemingly distinct worlds—network theory and machine learning—to reveal the universal laws of scalability that underlie both. It examines how value, capacity, and performance evolve as systems expand, offering a unified framework that connects Metcalfe’s Law with neural scaling laws.

By comparing network growth and model scaling, the book uncovers striking parallels: the diminishing throughput of densely connected networks mirrors the saturation of model generalization in large AI systems. Through rigorous analytical models, it explains when performance scales sublinearly, linearly, or even superlinearly—and why these transitions matter for the future of communication infrastructure and intelligent computation.

Designed for researchers and advanced practitioners in computer networks, information theory, and artificial intelligence, this work delivers both conceptual insight and practical guidance. It helps readers recognize the structural forces that shape scalability, the mathematical trade-offs between capacity and efficiency, and the design principles that can transfer between large-scale networks and learning systems.

Readers with backgrounds in probability, linear algebra, and algorithmic modeling will find this book a compelling synthesis of theory and application—a guide to understanding how scaling behavior defines the limits and possibilities of modern computational systems.

About this title

About the Author

Cheng Wang is a Professor at the School of Computer Science and Technology, Tongji University. His research focuses on large-scale communication networks, distributed systems, and intelligent computing—areas that closely relate to the study of system scalability explored in this book. Professor Wang has authored more than 150 peer-reviewed publications and holds 34 invention patents. He has led several national-level research initiatives, including the National Key Research and Development Program of the Ministry of Science and Technology and the Industrial Internet Innovation and Development Program of the Ministry of Industry and Information Technology. His academic contributions have been recognized with major distinctions such as the National Science and Technology Progress Award and the Outstanding Ph.D. Dissertation Award from the China Computer Federation (CCF). Professor Wang has published several books with Springer, including Human Factor Security and Safety (2025), Computational Structural Behavior (2026), and Integrated Security and Safety of Intelligent Computing (2026).

Yuhang Lin is a PhD candidate in the School of Computer Science and Technology at Tongji University. His research interests include information theory, behavioral computing and network representation learning.

From the Back Cover

This book bridges two seemingly distinct worlds—network theory and machine learning—to reveal the universal laws of scalability that underlie both. It examines how value, capacity, and performance evolve as systems expand, offering a unified framework that connects Metcalfe’s Law with neural scaling laws.

By comparing network growth and model scaling, the book uncovers striking parallels: the diminishing throughput of densely connected networks mirrors the saturation of model generalization in large AI systems. Through rigorous analytical models, it explains when performance scales sublinearly, linearly, or even superlinearly—and why these transitions matter for the future of communication infrastructure and intelligent computation.

Designed for researchers and advanced practitioners in computer networks, information theory, and artificial intelligence, this work delivers both conceptual insight and practical guidance. It helps readers recognize the structural forces that shape scalability, the mathematical trade-offs between capacity and efficiency, and the design principles that can transfer between large-scale networks and learning systems.

Readers with backgrounds in probability, linear algebra, and algorithmic modeling will find this book a compelling synthesis of theory and application—a guide to understanding how scaling behavior defines the limits and possibilities of modern computational systems.

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 9819581095
• ISBN 13: 9789819581092
• Binding: Hardcover
• Number of pages: 196