Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation

Jan Treur

doi:10.1007/978-3-030-88081-1_8

Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation

Jan Treur^*

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

In this paper, it is shown how, in contrast to often held beliefs, certain classes of nonlinear functions used for aggregation in network models enable analysis of the emerging within-network dynamics like linear functions do. In addition, two specific classes of nonlinear functions for aggregation in networks (weighted euclidean functions and weighted geometric functions) are introduced. Focusing on them in particular, it is illustrated in detail how methods for equilibrium analysis (based on a symbolic linear equation solver), can be applied to predict the state values in equilibria for such nonlinear cases as well.

Original language	English
Title of host publication	Computational Collective Intelligence
Subtitle of host publication	13th International Conference, ICCCI 2021, Rhodes, Greece, September 29 – October 1, 2021, Proceedings
Editors	Ngoc Thanh Nguyen, Lazaros Iliadis, Ilias Maglogiannis, Bogdan Trawiński
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	94-110
Number of pages	17
ISBN (Electronic)	9783030880811
ISBN (Print)	9783030880804
DOIs	https://doi.org/10.1007/978-3-030-88081-1_8
Publication status	Published - 2021
Event	13th International Conference on Computational Collective Intelligence, ICCCI 2021 - Virtual, Online Duration: 29 Sept 2021 → 1 Oct 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12876 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Computational Collective Intelligence, ICCCI 2021
City	Virtual, Online
Period	29/09/21 → 1/10/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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10.1007/978-3-030-88081-1_8

Treur2021_Chapter_EquilibriumAnalysisForWithin-NetworkDynamicsFinal published version, 1.1 MBLicence: Article 25fa Dutch Copyright Act

https://www.researchgate.net/publication/350693687

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Treur, J. (2021). Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation. In N. T. Nguyen, L. Iliadis, I. Maglogiannis, & B. Trawiński (Eds.), Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Rhodes, Greece, September 29 – October 1, 2021, Proceedings (pp. 94-110). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12876 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-88081-1_8

Treur, Jan. / Equilibrium Analysis for Within-Network Dynamics : From Linear to Nonlinear Aggregation. Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Rhodes, Greece, September 29 – October 1, 2021, Proceedings. editor / Ngoc Thanh Nguyen ; Lazaros Iliadis ; Ilias Maglogiannis ; Bogdan Trawiński. Springer Science and Business Media Deutschland GmbH, 2021. pp. 94-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, it is shown how, in contrast to often held beliefs, certain classes of nonlinear functions used for aggregation in network models enable analysis of the emerging within-network dynamics like linear functions do. In addition, two specific classes of nonlinear functions for aggregation in networks (weighted euclidean functions and weighted geometric functions) are introduced. Focusing on them in particular, it is illustrated in detail how methods for equilibrium analysis (based on a symbolic linear equation solver), can be applied to predict the state values in equilibria for such nonlinear cases as well.",

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year = "2021",

doi = "10.1007/978-3-030-88081-1_8",

language = "English",

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Treur, J 2021, Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation. in NT Nguyen, L Iliadis, I Maglogiannis & B Trawiński (eds), Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Rhodes, Greece, September 29 – October 1, 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12876 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 94-110, 13th International Conference on Computational Collective Intelligence, ICCCI 2021, Virtual, Online, 29/09/21. https://doi.org/10.1007/978-3-030-88081-1_8

Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation. / Treur, Jan.
Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Rhodes, Greece, September 29 – October 1, 2021, Proceedings. ed. / Ngoc Thanh Nguyen; Lazaros Iliadis; Ilias Maglogiannis; Bogdan Trawiński. Springer Science and Business Media Deutschland GmbH, 2021. p. 94-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12876 LNAI).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Treur J. Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation. In Nguyen NT, Iliadis L, Maglogiannis I, Trawiński B, editors, Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Rhodes, Greece, September 29 – October 1, 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 94-110. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2021 Sept 30. doi: 10.1007/978-3-030-88081-1_8

Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation

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