Imperial College London
Alternate

Nick S Jones

Faculty of Natural Sciences, Department of Mathematics

Professor of Mathematical Sciences
 
 
 
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Contact

 

+44 (0)20 7594 1146nick.jones

 
 
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Location

 

301aSir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Garrod:2021:10.1098/rsif.2021.0435,
author = {Garrod, M and Jones, N},
doi = {10.1098/rsif.2021.0435},
journal = {Journal of the Royal Society Interface},
pages = {1--12},
title = {Influencing dynamics on social networks without knowledge of network microstructure},
url = {http://dx.doi.org/10.1098/rsif.2021.0435},
volume = {18},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Social network based information campaigns can be used for promoting beneficial health behaviours and mitigating polarisation (e.g. regarding climate change or vaccines). Network-basedintervention strategies typically rely on full knowledge of network structure. It is largely not possibleor desirable to obtain population-level social network data due to availability and privacy issues. Itis easier to obtain information about individuals’ attributes (e.g. age, income), which are jointlyinformative of an individual’s opinions and their social network position. We investigate strategiesfor influencing the system state in a statistical mechanics based model of opinion formation. Using synthetic and data based examples we illustrate the advantages of implementing coarse-grainedinfluence strategies on Ising models with modular structure in the presence of external fields. Ourwork provides a scalable methodology for influencing Ising systems on large graphs and the firstexploration of the Ising influence problem in the presence of ambient (social) fields. By exploitingthe observation that strong ambient fields can simplify control of networked dynamics, our findingsopen the possibility of efficiently computing and implementing public information campaigns usinginsights from social network theory without costly or invasive levels of data collection.
AU  - Garrod,M
AU  - Jones,N
DO  - 10.1098/rsif.2021.0435
EP  - 12
PY  - 2021///
SN  - 1742-5662
SP  - 1
TI  - Influencing dynamics on social networks without knowledge of network microstructure
T2  - Journal of the Royal Society Interface
UR  - http://dx.doi.org/10.1098/rsif.2021.0435
UR  - https://royalsocietypublishing.org/doi/10.1098/rsif.2021.0435
UR  - http://hdl.handle.net/10044/1/90941
VL  - 18
ER  -
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