Scaling laws of network value from communication to learning Cheng Wang, Yuhang Lin

Format:

Book

Author/Creator:

Wang, Cheng (Of Tongji da xue (China)), author.

Lin, Yuhang, author.

Language:

English

Subjects (All):

Computer networks.

Neural networks (Computer science).

Deep learning (Machine learning).

Physical Description:

1 online resource

Place of Publication:

Singapore Springer [2026]

Summary:

"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"-- Springer Nature Link

Contents:

Scaling laws of self-organized communication networks : Throughput capacity

Scaling laws of self-organized communication networks : Transport complexity

Scaling laws of deep-learning neural networks : Taxonomy and survey

Scaling laws of deep-learning neural networks : Expressive power

Scaling laws of deep learning neural networks : Information loss

Notes:

Includes bibliographical references

Online resource; title from PDF title page (Springer Nature Link, viewed August 14, 2026)

Other Format:

Print version Wang, Cheng (Of Tongji da xue (China)) Scaling laws of network value

ISBN:

9789819581108

9819581109

OCLC:

1611101776

Access Restriction:

Restricted for use by site license