Sensing Enhancement on Complex Networks

Markus Brede; Guillermo  Romero Moreno

doi:10.1007/978-3-030-93413-2_30

Sensing Enhancement on Complex Networks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

Previous work has shown that communication between agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that whilst bestowing advantages on a small group of agents degree heterogeneity tends to impede overall sensing enhancement, while clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that for low connectivity sensing enhancement is maximised by random regular networks, whereas for large connectivity best sensing enhancement is found for ring graphs.

Original language	English
Title of host publication	Complex Networks Their Applications X
Subtitle of host publication	Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021
Editors	Rosa Maria Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis M. Rocha, Marta Sales-Pardo
Place of Publication	Cham
Publisher	Springer
Pages	353-364
Number of pages	12
Volume	2
Edition	1
ISBN (Electronic)	978-3-030-93413-2
ISBN (Print)	978-3-030-93415-6, 978-3-030-93412-5
DOIs	https://doi.org/10.1007/978-3-030-93413-2_30
Publication status	Published - 1 Jan 2022
Event	The 10th International Conference on Complex Networks and their Applications - Madrid, Spain Duration: 30 Nov 2021 → 2 Dec 2021 Conference number: 10 https://complexnetworks.org/

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer, Cham
Volume	2
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	The 10th International Conference on Complex Networks and their Applications
Abbreviated title	Complex Networks 2021
Country/Territory	Spain
City	Madrid
Period	30/11/21 → 2/12/21
Internet address	https://complexnetworks.org/

Keywords / Materials (for Non-textual outputs)

complex networks
opinion dynamics
sensing enhancement

Access to Document

10.1007/978-3-030-93413-2_30Licence: All Rights Reserved

https://link.springer.com/chapter/10.1007/978-3-030-93413-2_30Licence: All Rights Reserved

Cite this

Brede, M., & Romero Moreno, G. (2022). Sensing Enhancement on Complex Networks. In R. M. Benito, C. Cherifi, H. Cherifi, E. Moro, L. M. Rocha, & M. Sales-Pardo (Eds.), Complex Networks Their Applications X: Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021 (1 ed., Vol. 2, pp. 353-364). (Studies in Computational Intelligence; Vol. 2). Springer. https://doi.org/10.1007/978-3-030-93413-2_30

Brede, Markus ; Romero Moreno, Guillermo . / Sensing Enhancement on Complex Networks. Complex Networks Their Applications X: Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021. editor / Rosa Maria Benito ; Chantal Cherifi ; Hocine Cherifi ; Esteban Moro ; Luis M. Rocha ; Marta Sales-Pardo. Vol. 2 1. ed. Cham : Springer, 2022. pp. 353-364 (Studies in Computational Intelligence).

@inproceedings{116aae8678f4442397859671623ec970,

title = "Sensing Enhancement on Complex Networks",

abstract = "Previous work has shown that communication between agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that whilst bestowing advantages on a small group of agents degree heterogeneity tends to impede overall sensing enhancement, while clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that for low connectivity sensing enhancement is maximised by random regular networks, whereas for large connectivity best sensing enhancement is found for ring graphs.",

keywords = "complex networks, opinion dynamics, sensing enhancement",

author = "Markus Brede and {Romero Moreno}, Guillermo",

note = "Funding Information: The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. MB acknowledges support from the Alan Turing Institute (EPSRC grant EP/N510129/1) and the Royal Society (grant IES R2 192206). Funding Information: Acknowledgements. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. MB acknowledges support from the Alan Turing Institute (EPSRC grant EP/N510129/1) and the Royal Society (grant IES R2 192206). Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; The 10th International Conference on Complex Networks and their Applications, Complex Networks 2021 ; Conference date: 30-11-2021 Through 02-12-2021",

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Brede, M & Romero Moreno, G 2022, Sensing Enhancement on Complex Networks. in RM Benito, C Cherifi, H Cherifi, E Moro, LM Rocha & M Sales-Pardo (eds), Complex Networks Their Applications X: Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021. 1 edn, vol. 2, Studies in Computational Intelligence, vol. 2, Springer, Cham, pp. 353-364, The 10th International Conference on Complex Networks and their Applications, Madrid, Spain, 30/11/21. https://doi.org/10.1007/978-3-030-93413-2_30

Sensing Enhancement on Complex Networks. / Brede, Markus; Romero Moreno, Guillermo .
Complex Networks Their Applications X: Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021. ed. / Rosa Maria Benito; Chantal Cherifi; Hocine Cherifi; Esteban Moro; Luis M. Rocha; Marta Sales-Pardo. Vol. 2 1. ed. Cham: Springer, 2022. p. 353-364 (Studies in Computational Intelligence; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Sensing Enhancement on Complex Networks

AU - Brede, Markus

AU - Romero Moreno, Guillermo

N1 - Conference code: 10

PY - 2022/1/1

Y1 - 2022/1/1

N2 - Previous work has shown that communication between agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that whilst bestowing advantages on a small group of agents degree heterogeneity tends to impede overall sensing enhancement, while clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that for low connectivity sensing enhancement is maximised by random regular networks, whereas for large connectivity best sensing enhancement is found for ring graphs.

AB - Previous work has shown that communication between agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that whilst bestowing advantages on a small group of agents degree heterogeneity tends to impede overall sensing enhancement, while clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that for low connectivity sensing enhancement is maximised by random regular networks, whereas for large connectivity best sensing enhancement is found for ring graphs.

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Brede M, Romero Moreno G. Sensing Enhancement on Complex Networks. In Benito RM, Cherifi C, Cherifi H, Moro E, Rocha LM, Sales-Pardo M, editors, Complex Networks Their Applications X: Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021. 1 ed. Vol. 2. Cham: Springer. 2022. p. 353-364. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-93413-2_30