Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Impact of noise and network size in coupled maps with asymmetric influence amplification
Authors: Miniussi, Myriam
Ott, Thomas
Fellermann, Harold
et. al: No
Proceedings: Proceedings of the NOLTA 2020 Conference
Page(s): 282
Pages to: 285
Conference details: 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), Online Conference, 16-19 November 2020
Issue Date: Nov-2020
Language: English
Subjects: Coupled networks
Subject (DDC): 006: Special computer methods
Abstract: Studies of network dynamics are typically concerned either with the evolution of network topologies or with the dynamics of nodes in networks with static connectivity. Yet, in many important real-world systems, the interplay between network evolution and node dynamics gives rise to versatile behavior such as adaptive learning in neural systems or opinion dynamics in social systems. Here, we expand on previous work by Ott et al. [1] on synchronization of coupled maps with adaptive network connectivity. We investigate the dynamic regimes of this model by analyzing common network topology measures (shortest path, clustering coefficient, and node betweenness). We further evaluate the impact of network size on the observed dynamic behavior. Finally, we quantify the effect of additive noise on the system's ability to synchronize. We find node betweenness to capture some of the features obtained by node inequality and mutual information, while shortest path and clustering coefficient does not. Furthermore, our current investigations suggest that increasing network size limits the range of responses we can obtain from the model, and in some cases, severely impacts node synchronization. Additive noise, on the other hand, leads to an increase in glassy relaxation dynamics that enable the system to overcome metastable states. More work is needed to properly understand the complex dynamics arising from the model. Finally, we offer some input as to how this model could be useful to understand opinion formation in social environments.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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