LEADER 06096nam 22006975 450 001 9910366602303321 005 20250625174722.0 010 $a3-030-31445-6 024 7 $a10.1007/978-3-030-31445-3 035 $a(CKB)4100000009759126 035 $a(DE-He213)978-3-030-31445-3 035 $a(MiAaPQ)EBC5978079 035 $a(iGPub)SPNA0063505 035 $a(EXLCZ)994100000009759126 100 $a20191101d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNetwork-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models /$fby Jan Treur 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XVII, 412 p.) 225 1 $aStudies in Systems, Decision and Control,$x2198-4190 ;$v251 311 08$a3-030-31444-8 320 $aIncludes bibliographical references and index. 327 $aOn Adaptive Networks and Network Reification -- Ins and Outs of Network-Oriented Modeling -- A Unified Approach to Represent Network Adaptation Principles by Network Reification -- Modeling Higher-Order Network Adaptation by Multilevel Network Reification -- A Reified Network Model for Adaptive Decision Making Based on the Disconnect-Reconnect Adaptation Principle -- Using Multilevel Network Reification to Model Second-Order Adaptive Bonding by Homophily -- Reified Adaptive Network Models of Higher-Order Modeling a Strange Loop -- A Modeling Environment for Reified Temporal-Causal Network Models -- On the Universal Combination Function and the Universal Difference Equation for Reified Temporal-Causal Network Models -- Relating Network Emerging Behaviour to Network Structure -- Analysis of a Network?s Emerging Behaviour via its Structure Involving its Strongly Connected Components -- Relating a Reified Adaptive Network?s Structure to its Emerging Behaviour for Bonding by Homophily -- Relatinga Reified Adaptive Network?s Structure to its Emerging Behaviour for Hebbian learning -- Mathematical Details of Specific Difference and Differential Equations and Mathematical Analysis of Emerging Network Behaviour -- Using Network Reification for Adaptive Networks: Discussion. 330 $aThis book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-OrientedModeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master?s and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software. 410 0$aStudies in Systems, Decision and Control,$x2198-4190 ;$v251 606 $aDynamics 606 $aNonlinear theories 606 $aEngineering$xData processing 606 $aGraph theory 606 $aComputational intelligence 606 $aApplication software 606 $aApplied Dynamical Systems 606 $aData Engineering 606 $aGraph Theory 606 $aComputational Intelligence 606 $aComputer and Information Systems Applications 615 0$aDynamics. 615 0$aNonlinear theories. 615 0$aEngineering$xData processing. 615 0$aGraph theory. 615 0$aComputational intelligence. 615 0$aApplication software. 615 14$aApplied Dynamical Systems. 615 24$aData Engineering. 615 24$aGraph Theory. 615 24$aComputational Intelligence. 615 24$aComputer and Information Systems Applications. 676 $a620 700 $aTreur$b Jan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0753297 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366602303321 996 $aNetwork-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models$92530701 997 $aUNINA