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Competitive influence maximisation in social networks

Competitive influence maximisation in social networks
Competitive influence maximisation in social networks
Network-based interventions have shown immense potential in prompting behaviour changes in populations. Their implementation in the real world however, is often difficult and prone to failure as they are typically delivered on limited budgets and in many instances can be met with resistance in populations. Therefore, utilising available and limited resources optimally through careful and efficient planning is key for the successful implementation of any intervention. An important development in this aspect, is the influence maximisation framework —which lies at the interface of network science and computer science —and is commonly used to study network-based interventions in a theoretical setup with the aim of determining best practices that can optimise intervention outcomes in the real world. In this thesis, we explore the influence maximisation problem in a competitive setting (inspired by real-world conditions) where two contenders compete to maximise the spread of their intervention (or influence) in a social network. In its traditional form, the influence maximisation process identifies the k most influential nodes in a network —where k is given by a fixed budget. In this thesis, we propose the influence maximisation model with continuous distribution of influence where individuals are targeted heterogeneously based on their role in the influence spread process. This approach allows policymakers to obtain a detailed plan of the optimal distribution of budgets which is otherwise abstracted in traditional methods. In the rest of the thesis we use this approach to study multiple real-world settings.

University of Southampton
Chakraborty, Sukankana
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Chakraborty, Sukankana
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Stein, Sebastian
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Brede, Markus
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Chakraborty, Sukankana (2023) Competitive influence maximisation in social networks. University of Southampton, Doctoral Thesis, 139pp.

Record type: Thesis (Doctoral)

Abstract

Network-based interventions have shown immense potential in prompting behaviour changes in populations. Their implementation in the real world however, is often difficult and prone to failure as they are typically delivered on limited budgets and in many instances can be met with resistance in populations. Therefore, utilising available and limited resources optimally through careful and efficient planning is key for the successful implementation of any intervention. An important development in this aspect, is the influence maximisation framework —which lies at the interface of network science and computer science —and is commonly used to study network-based interventions in a theoretical setup with the aim of determining best practices that can optimise intervention outcomes in the real world. In this thesis, we explore the influence maximisation problem in a competitive setting (inspired by real-world conditions) where two contenders compete to maximise the spread of their intervention (or influence) in a social network. In its traditional form, the influence maximisation process identifies the k most influential nodes in a network —where k is given by a fixed budget. In this thesis, we propose the influence maximisation model with continuous distribution of influence where individuals are targeted heterogeneously based on their role in the influence spread process. This approach allows policymakers to obtain a detailed plan of the optimal distribution of budgets which is otherwise abstracted in traditional methods. In the rest of the thesis we use this approach to study multiple real-world settings.

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Chakraborty Doctoral Thesis
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Published date: 4 January 2023

Identifiers

Local EPrints ID: 472894
URI: http://eprints.soton.ac.uk/id/eprint/472894
PURE UUID: 912dff69-98a0-4867-96ea-9c81d8bd1acf
ORCID for Sukankana Chakraborty: ORCID iD orcid.org/0000-0002-2115-8531
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857

Catalogue record

Date deposited: 05 Jan 2023 17:54
Last modified: 25 May 2023 01:48

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Contributors

Author: Sukankana Chakraborty ORCID iD
Thesis advisor: Sebastian Stein ORCID iD
Thesis advisor: Markus Brede

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