The Science of Complex Networks : A Journey Through Random Graphs and Network Science

Edward H. Whitmore

ISBN 13: 9798266479678

Published by Createspace Independent Publishing Platform Sep 2025, 2025

About this Item

Neuware - The Science of Complex Networks provides a thorough and practical introduction to the fascinating world of network science. Starting with foundational random graph models such as Erdos-Rényi, scale-free, small-world, and configuration models, it moves into advanced frameworks including exponential random graphs, stochastic block models, and multilayer networks.The book explores both static network structures and dynamic processes, covering diffusion, epidemic spreading, opinion dynamics, synchronization, and cascading failures, with a focus on how network topology shapes these behaviors. Temporal networks, where connections evolve over time, are examined alongside resilience and robustness, showing how systems withstand and recover from disruptions.Readers are guided through clear explanations, mathematical formulations, and hands-on simulation techniques using tools like NetworkX. Real-world examples from disease control to infrastructure design and information spread illustrate how network science applies across social, biological, and technological domains.This book equips readers to understand, model, and analyze complex networks while gaining insights into practical applications.Topics covered include: - Foundational random graph models: Erdos-Rényi, scale-free, small-world, configuration models- Advanced network frameworks: exponential random graphs, stochastic block models, multilayer networks- Dynamic processes on networks: diffusion, epidemic spreading, opinion dynamics, synchronization, cascading failures- Temporal networks and evolving connections- Network resilience and robustness- Practical simulation techniques using Python and NetworkX- Applications in social, biological, and technological systems.

Seller Inventory # 9798266479678

Bibliographic Details

• Title: The Science of Complex Networks : A Journey Through Random Graphs and Network Science
• Publisher: Createspace Independent Publishing Platform Sep 2025
• Publication Date: 2025
• Language: English
• ISBN 13: 9798266479678
• Binding: Taschenbuch
• Illustrator: Monroe, Eli
• Condition: Neu
• Dimensions: 229W x 152H x 14D millimeters 229 millimeters width by 152 millimeters height by 14 millimeters depth
• Weight: 280 grams

About this title

Synopsis

The Science of Complex Networks provides a thorough and practical introduction to the fascinating world of network science. Starting with foundational random graph models such as Erdős–Rényi, scale-free, small-world, and configuration models, it moves into advanced frameworks including exponential random graphs, stochastic block models, and multilayer networks.

The book explores both static network structures and dynamic processes, covering diffusion, epidemic spreading, opinion dynamics, synchronization, and cascading failures, with a focus on how network topology shapes these behaviors. Temporal networks, where connections evolve over time, are examined alongside resilience and robustness, showing how systems withstand and recover from disruptions.

Readers are guided through clear explanations, mathematical formulations, and hands-on simulation techniques using tools like NetworkX. Real-world examples from disease control to infrastructure design and information spread illustrate how network science applies across social, biological, and technological domains.

This book equips readers to understand, model, and analyze complex networks while gaining insights into practical applications.

Topics covered include:

• Foundational random graph models: Erdős–Rényi, scale-free, small-world, configuration models

• Advanced network frameworks: exponential random graphs, stochastic block models, multilayer networks

• Dynamic processes on networks: diffusion, epidemic spreading, opinion dynamics, synchronization, cascading failures

• Temporal networks and evolving connections

• Network resilience and robustness

• Practical simulation techniques using Python and NetworkX

• Applications in social, biological, and technological systems