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Competitive influence maximisation with nonlinear cost of allocations

Competitive influence maximisation with nonlinear cost of allocations
Competitive influence maximisation with nonlinear cost of allocations
We explore the competitive influence maximisation problem in the voter model. We extend past work by modelling real-world settings where the strength of influence changes nonlinearly with external allocations to the network. We use this approach to identify two distinct regimes — one where optimal intervention strategies offer significant gain in outcomes, and the other where they yield no gains. The two regimes also vary in their sensitivity to budget availability, and we find that in some cases, even a tenfold increase in the budget only marginally improves the outcome of an intervention in a population.
Influence maximization, Social networks
247–258
Springer Cham
Chakraborty, Sukankana
f0a805bd-745b-48ab-b7cd-b054ab0a67d3
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Dinh, T.N.
Li, M.
Chakraborty, Sukankana
f0a805bd-745b-48ab-b7cd-b054ab0a67d3
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Dinh, T.N.
Li, M.

Chakraborty, Sukankana and Stein, Sebastian (2023) Competitive influence maximisation with nonlinear cost of allocations. Dinh, T.N. and Li, M. (eds.) In Computational Data and Social Networks. vol. 13831, Springer Cham. 247–258 . (doi:10.1007/978-3-031-26303-3_22).

Record type: Conference or Workshop Item (Paper)

Abstract

We explore the competitive influence maximisation problem in the voter model. We extend past work by modelling real-world settings where the strength of influence changes nonlinearly with external allocations to the network. We use this approach to identify two distinct regimes — one where optimal intervention strategies offer significant gain in outcomes, and the other where they yield no gains. The two regimes also vary in their sensitivity to budget availability, and we find that in some cases, even a tenfold increase in the budget only marginally improves the outcome of an intervention in a population.

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Accepted/In Press date: 28 October 2022
Published date: 11 February 2023
Venue - Dates: The 11th International Conference on Computational Data and Social Networks, Florida, United States, 2022-12-05 - 2022-12-07
Keywords: Influence maximization, Social networks
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Identifiers

Local EPrints ID: 474023
URI: http://eprints.soton.ac.uk/id/eprint/474023
DOI: doi:10.1007/978-3-031-26303-3_22
PURE UUID: 2c9e6856-ff40-4846-9d39-a76e5047d4f7
ORCID for Sukankana Chakraborty: ORCID iD orcid.org/0000-0002-2115-8531
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857

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Date deposited: 09 Feb 2023 17:42
Last modified: 18 Mar 2024 05:03

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Contributors

Author: Sukankana Chakraborty ORCID iD
Author: Sebastian Stein ORCID iD
Editor: T.N. Dinh
Editor: M. Li

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