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Competitive influence maximisation using voting dynamics

Competitive influence maximisation using voting dynamics
Competitive influence maximisation using voting dynamics
We identify optimal strategies for maximising influence within a social network in competitive settings under budget constraints. While existing work has focussed on simple threshold models, we consider more realistic settings, where (i) states are dynamic, i.e., nodes oscillate between influenced and uninfluenced states, and (ii) continuous amounts of resources (e.g., incentives or effort) can be expended on the nodes. We propose a mathematical model using voting dynamics to characterise optimal strategies in a prototypical star topology against known and unknown adversarial strategies. In cases where the adversarial strategy is unknown, we characterise the Nash Equilibrium. To generalise the work further, we introduce a fixed cost incurred to gain access to nodes, together with the dynamic cost proportional to the influence exerted on the nodes, constrained by the same budget. We observe that, as the cost changes, the system interpolates between the historic discrete and the current continuous case.
978-985
Chakraborty, Sukankana
f0a805bd-745b-48ab-b7cd-b054ab0a67d3
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Restocchi, Valerio
39654f4e-2a84-4c78-a853-081704568415
Swami, Ananthram
6a3932ba-47f7-4d19-8f5e-c4e1b09fd492
de Mel, Geeth
7091bff7-b736-44e7-9ccb-4a748fb9e729
Chakraborty, Sukankana
f0a805bd-745b-48ab-b7cd-b054ab0a67d3
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Restocchi, Valerio
39654f4e-2a84-4c78-a853-081704568415
Swami, Ananthram
6a3932ba-47f7-4d19-8f5e-c4e1b09fd492
de Mel, Geeth
7091bff7-b736-44e7-9ccb-4a748fb9e729

Chakraborty, Sukankana, Stein, Sebastian, Brede, Markus, Restocchi, Valerio, Swami, Ananthram and de Mel, Geeth (2019) Competitive influence maximisation using voting dynamics. Workshop on Social Influence held in conjunction with the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, , Vancouver, Canada. 27 - 30 Aug 2019. pp. 978-985 . (doi:10.1145/3341161.3345025).

Record type: Conference or Workshop Item (Paper)

Abstract

We identify optimal strategies for maximising influence within a social network in competitive settings under budget constraints. While existing work has focussed on simple threshold models, we consider more realistic settings, where (i) states are dynamic, i.e., nodes oscillate between influenced and uninfluenced states, and (ii) continuous amounts of resources (e.g., incentives or effort) can be expended on the nodes. We propose a mathematical model using voting dynamics to characterise optimal strategies in a prototypical star topology against known and unknown adversarial strategies. In cases where the adversarial strategy is unknown, we characterise the Nash Equilibrium. To generalise the work further, we introduce a fixed cost incurred to gain access to nodes, together with the dynamic cost proportional to the influence exerted on the nodes, constrained by the same budget. We observe that, as the cost changes, the system interpolates between the historic discrete and the current continuous case.

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Competitive Influence Maximisation using Voting Dynamics - Accepted Manuscript
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Accepted/In Press date: 2019
Published date: 27 August 2019
Venue - Dates: Workshop on Social Influence held in conjunction with the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, , Vancouver, Canada, 2019-08-27 - 2019-08-30

Identifiers

Local EPrints ID: 432969
URI: http://eprints.soton.ac.uk/id/eprint/432969
PURE UUID: e37fa748-1938-4874-8a3b-dd86def8f1f4
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: 05 Aug 2019 16:30
Last modified: 16 Mar 2024 03:57

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Contributors

Author: Sukankana Chakraborty ORCID iD
Author: Sebastian Stein ORCID iD
Author: Markus Brede
Author: Valerio Restocchi
Author: Ananthram Swami
Author: Geeth de Mel

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