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Network Science with Python: Modeling Complex Systems in Finance, Infrastructure, and Society
Paperback
General MathematicsGeneral SociologyProgramming
Publisher: Independently Published
Published: Mar 16 2026
Pages: 550
Weight: 1.60
Height: 1.11 Width: 6.00 Depth: 9.00
Language: English
Modern systems rarely operate in isolation. Financial markets, power grids, transportation networks, supply chains, and online communities are all built on interconnected structures whose behavior emerges from the relationships between nodes.
Network Science with Python explores how these complex systems can be modeled, analyzed, and understood using practical computational tools.
This book introduces the mathematical foundations of network science and shows how Python can be used to investigate real-world systems where structure, connectivity, and influence shape outcomes.
Readers will learn how to build and analyze networks representing financial systems, infrastructure, and large social structures while developing an intuitive understanding of how networks evolve, fail, and recover.
Inside the book you will explore:
Core principles of graph theory and network structure
Centrality, clustering, and community detection
Modeling financial contagion and systemic risk
Infrastructure networks such as transportation, energy, and logistics
Diffusion processes including information and shock propagation
Network resilience, robustness, and cascading failures
Building and analyzing networks using Python and modern scientific libraries
Through practical examples and computational demonstrations, the book connects theory with implementation, helping readers translate network concepts into working analytical tools.
Whether analyzing financial stability, infrastructure resilience, or complex organizational systems, network science provides a powerful framework for understanding how interconnected systems behave.
This guide offers a structured introduction to those methods while demonstrating how Python can be used to explore complex networks in real-world contexts.
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