A unified approach to represent network adaptation principles by network reification

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

In this chapter, the notion of network reification is introduced: a construction by which a given (base) network is extended by adding explicit states representing the characteristics defining the base network’s structure. This is explained for temporal-causal networks where connection weights, combination functions, and speed factors represent the characteristics for Connectivity, Aggregation, and Timing describing the network structure. Having the network structure represented in an explicit manner within the extended network enables to model the adaptation of the base network by dynamics within the reified network: an adaptive network is represented by a non-adaptive network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated for a number of well-known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks.

Original languageEnglish
Title of host publicationNetwork-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models
EditorsJan Treur
PublisherSpringer International Publishing AG
Chapter3
Pages59-98
Number of pages40
DOIs
Publication statusPublished - 1 Jan 2020

Publication series

NameStudies in Systems, Decision and Control
Volume251
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

Fingerprint

reification
Agglomeration
Network Structure
Reification
Hebbian Learning
Network Evolution
Social Networks
Aggregation
Connectivity
aggregation

Cite this

Treur, J. (2020). A unified approach to represent network adaptation principles by network reification. In J. Treur (Ed.), Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models (pp. 59-98). (Studies in Systems, Decision and Control; Vol. 251). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-31445-3_3
Treur, Jan. / A unified approach to represent network adaptation principles by network reification. Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models . editor / Jan Treur. Springer International Publishing AG, 2020. pp. 59-98 (Studies in Systems, Decision and Control).
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Treur, J 2020, A unified approach to represent network adaptation principles by network reification. in J Treur (ed.), Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models . Studies in Systems, Decision and Control, vol. 251, Springer International Publishing AG, pp. 59-98. https://doi.org/10.1007/978-3-030-31445-3_3

A unified approach to represent network adaptation principles by network reification. / Treur, Jan.

Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models . ed. / Jan Treur. Springer International Publishing AG, 2020. p. 59-98 (Studies in Systems, Decision and Control; Vol. 251).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

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Treur J. A unified approach to represent network adaptation principles by network reification. In Treur J, editor, Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models . Springer International Publishing AG. 2020. p. 59-98. (Studies in Systems, Decision and Control). https://doi.org/10.1007/978-3-030-31445-3_3