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Learning interpretable collective variables for spreading processes on networks

Title data

Lücke, Marvin ; Winkelmann, Stefanie ; Heitzig, Jobst ; Molkenthin, Nora ; Koltai, Peter:
Learning interpretable collective variables for spreading processes on networks.
In: Physical Review E. Vol. 109 (2024) Issue 2 . - L022301.
ISSN 2470-0053
DOI: https://doi.org/10.1103/PhysRevE.109.L022301

Abstract in another language

Collective variables (CVs) are low-dimensional projections of high-dimensional system states. They are used to gain insights into complex emergent dynamical behaviors of processes on networks. The relation between CVs and network measures is not well understood and its derivation typically requires detailed knowledge of both the dynamical system and the network topology. In this Letter, we present a data-driven method for algorithmically learning and understanding CVs for binary-state spreading processes on networks of arbitrary topology. We demonstrate our method using four example networks: the stochastic block model, a ring-shaped graph, a random regular graph, and a scale-free network generated by the Albert-Barabási model. Our results deliver evidence for the existence of low-dimensional CVs even in cases that are not yet understood theoretically.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Dynamical Systems and Data > Chair Dynamical Systems and Data - Univ.-Prof. Dr. Peter Koltai
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Dynamical Systems and Data
Result of work at the UBT: No
DDC Subjects: 500 Science > 510 Mathematics
500 Science > 530 Physics
Date Deposited: 20 Jul 2023 05:24
Last Modified: 08 Feb 2024 08:46
URI: https://eref.uni-bayreuth.de/id/eprint/86184