Complex Networks and Control Theory : An Introduction to Structural Controllability and Its Biological Applications

Complex Networks and Control Theory: An Introduction to Structural Controllability and Its Biological Applications (Studies in Systems, Decision and Control, 680)

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Complex Networks and Control Theory: An Introduction to Structural Controllability and Its Biological Applications (Studies in Systems, Decision and Control, 680)

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Complex Networks and Control Theory : An Introduction to Structural Controllability and Its Biological Applications

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Complex Networks and Control Theory: An Introduction to Structural Controllability and Its Biological Applications (Studies in Systems, Decision and Control, 680)

Overall Rating: 2.3 / 5 (average from multiple review sources, as of 23 Jun 2026)
Based on a total of 48,964 customer reviews from independent review platforms.

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The values are derived from publicly available retailer ratings from platforms such as Feefo, http://Reviews.io , Trustpilot, and others, and are aggregated monthly.

All brand names and logos are the property of their respective owners.

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Complex Networks and Control Theory : An Introduction to Structural Controllability and Its Biological Applications

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This book explores the intersection of control theory and complex network science through the framework of structural controllability of complex networks.It introduces the main concepts, mathematical formulations, and computational algorithms needed to analyze the controllability of large-scale networked systems.Covering topics from basic control theory and graph-based methods to mathematical models and data-driven approaches, the book describes current applications of network controllability in social, engineering, and biological systems, with particular emphasis on biological networks.It thus offers an interdisciplinary view of a rapidly developing research area and highlights the growing importance of controllability analysis in complex real-world systems.It will be useful to researchers and graduate students in network science, systems biology, control theory, robotics, and related areas of engineering.
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