Competitive influence maximisation in social networks

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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Identifiers

ORCID for Sukankana Chakraborty: orcid.org/0000-0002-2115-8531

ORCID for Sebastian Stein: orcid.org/0000-0003-2858-8857

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