Accelerating convergence of inference in the inverse Ising problem
Accelerating convergence of inference in the inverse Ising problem
Inferring modelling parameters of dynamical processes from observational data is an important inverse problem in statistical physics. In this paper, instead of passively observing the dynamics for inference, we focus on strategically manipulating dynamics to generate data that gives more accurate estimators within fewer observations. For this purpose, we consider the inference problem rooted in the Ising model with two opposite external fields, assuming that the strength distribution of one of the fields (labelled as passive) is unknown and needs to be inferred. In contrast, the other field (labelled active) is strategically deployed to interact with the Ising dynamics in such a way as to improve the accuracy of estimates of inferring the opposing passive field. By comparing to benchmark cases, we first demonstrate that it is possible to accelerate the inference by strategically interacting with the Ising dynamics. We then apply series expansions to obtain an approximation of the optimized influence configurations in the high-temperature region. Furthermore, by using mean-field estimates, we also demonstrate the applicability of the method in a more general scenario where real-time tracking of the system is infeasible. Last, analysing the optimized influence profiles, we describe heuristics for manipulating the Ising dynamics for faster inference. For example, we show that agents targeted more strongly by the passive field should also be strongly targeted by the active one.
Complex Networks, Inverse Kinetic Ising Problem, Network Control, Network Inference, Network inference, Complex networks, Inverse kinetic Ising problem, Network control
Cai, Zhongqi
b3ce4c1b-e545-4a86-9592-960542756e14
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
15 December 2023
Cai, Zhongqi
b3ce4c1b-e545-4a86-9592-960542756e14
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Cai, Zhongqi, Gerding, Enrico and Brede, Markus
(2023)
Accelerating convergence of inference in the inverse Ising problem.
Physica A: Statistical Mechanics and its Applications, 632 (Part 1), [129348].
(doi:10.1016/j.physa.2023.129348).
Abstract
Inferring modelling parameters of dynamical processes from observational data is an important inverse problem in statistical physics. In this paper, instead of passively observing the dynamics for inference, we focus on strategically manipulating dynamics to generate data that gives more accurate estimators within fewer observations. For this purpose, we consider the inference problem rooted in the Ising model with two opposite external fields, assuming that the strength distribution of one of the fields (labelled as passive) is unknown and needs to be inferred. In contrast, the other field (labelled active) is strategically deployed to interact with the Ising dynamics in such a way as to improve the accuracy of estimates of inferring the opposing passive field. By comparing to benchmark cases, we first demonstrate that it is possible to accelerate the inference by strategically interacting with the Ising dynamics. We then apply series expansions to obtain an approximation of the optimized influence configurations in the high-temperature region. Furthermore, by using mean-field estimates, we also demonstrate the applicability of the method in a more general scenario where real-time tracking of the system is infeasible. Last, analysing the optimized influence profiles, we describe heuristics for manipulating the Ising dynamics for faster inference. For example, we show that agents targeted more strongly by the passive field should also be strongly targeted by the active one.
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Accelerating Convergence of Inference in the Inverse Ising Problem
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Accepted/In Press date: 29 October 2023
e-pub ahead of print date: 31 October 2023
Published date: 15 December 2023
Additional Information:
Funding Information:
The authors acknowledge the use of the IRIDIS High Performance Computing Facility in the completion of this work. ZC acknowledges support from China Scholarships Council (No. 201906310134 ). MB acknowledges support from the Alan Turing Institute, United Kingdom (EPSRC grant EP/N510129/1 , https://www.turing.ac.uk/ ) and the Royal Society, United Kingdom (grant IES\R2\192206 , https://royalsociety.org/ ).
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© 2023 The Author(s)
Keywords:
Complex Networks, Inverse Kinetic Ising Problem, Network Control, Network Inference, Network inference, Complex networks, Inverse kinetic Ising problem, Network control
Identifiers
Local EPrints ID: 484073
URI: http://eprints.soton.ac.uk/id/eprint/484073
ISSN: 0378-4371
PURE UUID: a0b32d68-c110-4da1-84e1-77cf315d741a
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Date deposited: 09 Nov 2023 18:09
Last modified: 18 Mar 2024 03:02
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Author:
Zhongqi Cai
Author:
Enrico Gerding
Author:
Markus Brede
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