Influencing dynamics on social networks without knowledge of network microstructure
File(s)
Accepted version
Author(s)
Garrod, Matthew
Jones, Nicholas
Type
Journal Article
Abstract
Social network based information campaigns can be used for promoting beneficial health behaviours and mitigating polarisation (e.g. regarding climate change or vaccines). Network-based
intervention strategies typically rely on full knowledge of network structure. It is largely not possible
or desirable to obtain population-level social network data due to availability and privacy issues. It
is easier to obtain information about individuals’ attributes (e.g. age, income), which are jointly
informative of an individual’s opinions and their social network position. We investigate strategies
for 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-grained
influence strategies on Ising models with modular structure in the presence of external fields. Our
work provides a scalable methodology for influencing Ising systems on large graphs and the first
exploration of the Ising influence problem in the presence of ambient (social) fields. By exploiting
the observation that strong ambient fields can simplify control of networked dynamics, our findings
open the possibility of efficiently computing and implementing public information campaigns using
insights from social network theory without costly or invasive levels of data collection.
intervention strategies typically rely on full knowledge of network structure. It is largely not possible
or desirable to obtain population-level social network data due to availability and privacy issues. It
is easier to obtain information about individuals’ attributes (e.g. age, income), which are jointly
informative of an individual’s opinions and their social network position. We investigate strategies
for 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-grained
influence strategies on Ising models with modular structure in the presence of external fields. Our
work provides a scalable methodology for influencing Ising systems on large graphs and the first
exploration of the Ising influence problem in the presence of ambient (social) fields. By exploiting
the observation that strong ambient fields can simplify control of networked dynamics, our findings
open the possibility of efficiently computing and implementing public information campaigns using
insights from social network theory without costly or invasive levels of data collection.
Date Issued
2021-08-25
Date Acceptance
2021-07-27
Citation
Journal of the Royal Society Interface, 2021, 18 (181), pp.1-12
ISSN
1742-5662
Publisher
The Royal Society
Start Page
1
End Page
12
Journal / Book Title
Journal of the Royal Society Interface
Volume
18
Issue
181
Copyright Statement
© 2021 The Author(s)
Published by the Royal Society. All rights reserved.
Published by the Royal Society. All rights reserved.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering and Physical Sciences Research Council (EPSRC)
Engineering and Physical Sciences Research Council
Identifier
https://royalsocietypublishing.org/doi/10.1098/rsif.2021.0435
Grant Number
EP/N014529/1
EP/L016613/1
EP/L016613/1
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
social networks
opinion dynamics
statistical physics
optimization
INTERVENTION
BLOCKMODELS
PHYSICS
IMPACT
MODEL
opinion dynamics
optimization
social networks
statistical physics
General Science & Technology
Publication Status
Published
Date Publish Online
2021-08-25