Publication Date

Spring 2023

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Katerina Potika

Second Advisor

William Andreopoulos

Third Advisor

Navrati Saxena

Keywords

Influence maximization, CELF algorithm, greedy dominating set

Abstract

An online platform where various people come together to share information and communicate is called a social network. These platforms are set apart from other means of communication mostly because you can follow and interact also with different people even some you never met, comment on their posts, and re-sharing their posts. Companies such as Amazon and Walmart use these platforms daily for marketing purposes, like spreading information regarding new products and services they offer. They carefully select a subset of users, called influencers, who are usually the ones with high influence over the rest of the users. Influencers receive an incentive, like a free product, in order to spread the information for the specific company. The starting set of such influencers is limited, and they are responsible to use their connections to spread the product either directly through making posts and commenting or indirectly by using the product on social networks.

In this project, we explore two approaches. In the first approach, we calculate a set of possible influencers by relying on a greedy dominating set approach as a preprocessing step and decide which ones are maximizing the spread through CELF algorithm [1] for the linear threshold model. In the second approach, we first detect communities on the network, and then find influencers inside each community using various influencer selection methods, like the greedy dominating set, and some node centralities. We contact experiments on four real datasets and measure the total number of nodes receiving the information under our approaches and existing approaches.

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