Dynamics On and Of Complex Networks III, Fakhteh Ghanbarnejad; Rishiraj Saha Roy; Fariba Ka

Автор: David P. Rosin
Название: Dynamics of Complex Autonomous Boolean Networks
ISBN: 3319135775 ISBN-13(EAN): 9783319135779
Издательство: Springer
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Цена: 111790.00 T
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Описание: This thesis focuses on the dynamics of autonomous Boolean networks, on the basis of Boolean logic functions in continuous time without external clocking.

Автор: David P. Rosin
Название: Dynamics of Complex Autonomous Boolean Networks
ISBN: 3319367064 ISBN-13(EAN): 9783319367064
Издательство: Springer
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Цена: 87060.00 T
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Описание: This thesis focuses on the dynamics of autonomous Boolean networks, on the basis of Boolean logic functions in continuous time without external clocking.

Автор: Nigel Gilbert; Petra Ahrweiler; Andreas Pyka
Название: Simulating Knowledge Dynamics in Innovation Networks
ISBN: 3662511487 ISBN-13(EAN): 9783662511480
Издательство: Springer
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Цена: 121110.00 T
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Описание: This text introduces SKIN (Simulation Knowledge Dynamics in Innovation Networks), an agent-based simulation model that primarily focuses on joint knowledge creation and exchange of knowledge in innovation co-operations and networks.

Автор: Michael Golosovsky
Название: Citation Analysis and Dynamics of Citation Networks
ISBN: 303028168X ISBN-13(EAN): 9783030281687
Издательство: Springer
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Цена: 55890.00 T
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Описание: This book deals with the science of science by applying network science methods to citation networks and uniquely presents a physics-inspired model of citation dynamics. This stochastic model of citation dynamics is based on a well-known copying or recursive search mechanism. The measurements covered in this text yield parameters of the model and reveal that citation dynamics of scientific papers is not linear, as was previously assumed. This nonlinearity has far-reaching consequences including non-stationary citation distributions, diverging citation trajectories of similar papers, and runaways or 'immortal papers' with an infinite citation lifespan. The author shows us that nonlinear stochastic models of citation dynamics can be the basis for a quantitative probabilistic prediction of citation dynamics of individual papers and of the overall journal impact factor. This book appeals to students and researchers from differing subject areas working in network science and bibliometrics.

Автор: Ivan Zelinka; Guanrong Chen
Название: Evolutionary Algorithms, Swarm Dynamics and Complex Networks
ISBN: 3662556618 ISBN-13(EAN): 9783662556610
Издательство: Springer
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Цена: 139750.00 T
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Описание: In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks.

Автор: Ivan Zelinka; Guanrong Chen
Название: Evolutionary Algorithms, Swarm Dynamics and Complex Networks
ISBN: 3662572478 ISBN-13(EAN): 9783662572474
Издательство: Springer
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Цена: 139750.00 T
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Описание: Evolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), which are usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects. 

Автор: Fabio Fagnani; Paolo Frasca
Название: Introduction to Averaging Dynamics over Networks
ISBN: 3319680218 ISBN-13(EAN): 9783319680217
Издательство: Springer
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Цена: 102480.00 T
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Описание: This book deals with averaging dynamics, a paradigmatic example of network based dynamics in multi-agent systems.

Автор: Mengbin Ye
Название: Opinion Dynamics and the Evolution of Social Power in Social Networks
ISBN: 3030106055 ISBN-13(EAN): 9783030106058
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book uses rigorous mathematical analysis to advance opinion dynamics models for social networks in three major directions. First, a novel model is proposed to capture how a discrepancy between an individual’s private and expressed opinions can develop due to social pressures that arise in group situations or through extremists deliberately shaping public opinion. Detailed theoretical analysis of the final opinion distribution is followed by use of the model to study Asch’s seminal experiments on conformity, and the phenomenon of pluralistic ignorance. Second, the DeGroot-Friedkin model for evolution of an individual’s social power (self-confidence) is developed in a number of directions. The key result establishes that an individual’s initial social power is forgotten exponentially fast, even when the network changes over time; eventually, an individual’s social power depends only on the (changing) network structure. Last, a model for the simultaneous discussion of multiple logically interdependent topics is proposed. To ensure that a consensus across the opinions of all individuals is achieved, it turns out that the interpersonal interactions must be weaker than an individual’s introspective cognitive process for establishing logical consistency among the topics. Otherwise, the individual may experience cognitive overload and the opinion system becomes unstable. Conclusions of interest to control engineers, social scientists, and researchers from other relevant disciplines are discussed throughout the thesis with support from both social science and control literature.

Автор: Alexander Mehler; Andy L?cking; Sven Banisch; Phil
Название: Towards a Theoretical Framework for Analyzing Complex Linguistic Networks
ISBN: 3662472376 ISBN-13(EAN): 9783662472378
Издательство: Springer
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Цена: 139310.00 T
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Описание: The aim of this book is to advocate and promote network models of linguistic systems that are both based on thorough mathematical models and substantiated in terms of linguistics.

Автор: Jinhu L?; Xinghuo Yu; Guanrong Chen; Wenwu Yu
Название: Complex Systems and Networks
ISBN: 3662478234 ISBN-13(EAN): 9783662478233
Издательство: Springer
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Цена: 139310.00 T
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Описание: In particular, a general model for studying time evolution of transition networks, deflection routing in complex networks, recommender systems for social networks analysis and mining, strategy selection in networked evolutionary games, integration and methods in computational biology, are discussed in detail.

Автор: Judith Lehnert
Название: Controlling Synchronization Patterns in Complex Networks
ISBN: 3319251139 ISBN-13(EAN): 9783319251134
Издательство: Springer
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Цена: 111790.00 T
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Описание: This research aims to achieve a fundamental understanding of synchronization and its interplay with the topology of complex networks. Besides zero-lag synchrony, group and cluster states are considered, enabling a description and study of complex synchronization patterns within the presented theory.

Автор: Nigel Gilbert; Petra Ahrweiler; Andreas Pyka
Название: Simulating Knowledge Dynamics in Innovation Networks
ISBN: 3662435071 ISBN-13(EAN): 9783662435076
Издательство: Springer
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Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text introduces SKIN (Simulation Knowledge Dynamics in Innovation Networks), an agent-based simulation model that primarily focuses on joint knowledge creation and exchange of knowledge in innovation co-operations and networks.