Influence maximisation towards target users and minimal diffusion of information based on information needs

Influence maximisation within social network is essential to the modern business. Influence Maximisation Problem (IMP) involves the minimal selection of influencers that leads to maximum contagion while minimizing Diffusion Cost (DC). Previous models of IMP do not consider DC in spreading informatio...

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Main Author:	Temitope, Olanrewaju Abdus-Samad
Format:	Thesis
Language:	eng eng aa
Published:	2020
Subjects:	T58.6-58.62 Management information systems
Online Access:	https://etd.uum.edu.my/8398/1/s901087_01.pdf https://etd.uum.edu.my/8398/2/s901087_02.pdf https://etd.uum.edu.my/8398/3/s901087%20references.docx
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id	my-uum-etd.8398
record_format	uketd_dc
spelling	my-uum-etd.83982022-06-01T08:08:03Z Influence maximisation towards target users and minimal diffusion of information based on information needs 2020 Temitope, Olanrewaju Abdus-Samad Ahmad, Rahayu Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences T58.6-58.62 Management information systems Influence maximisation within social network is essential to the modern business. Influence Maximisation Problem (IMP) involves the minimal selection of influencers that leads to maximum contagion while minimizing Diffusion Cost (DC). Previous models of IMP do not consider DC in spreading information towards target users. Furthermore, influencer selection for varying information needs was not considered which leads to influence overlaps and elimination of weak nodes. This study proposes the Information Diffusion towards Target Users (IDTU) algorithm to enhance influencer selection while minimizing the DC. IDTU was developed on greedy approach by using graph sketches to improve the selection of influencers that maximize influence spread to a set of target users. Moreover, the influencer identification based on specific needs was implemented using a General Additive Model on four fundamental centralities. Experimental method was used by employing five social network datasets including Epinions, Wiki-Vote, SlashDot, Facebook and Twitter from Stanford data repository. Evaluation on IDTU was performed against 3 greedy and 6 heuristics benchmark algorithms. IDTU identified all the specified target nodes while lowering the DC by up to 79%. In addition, the influence overlap problem was reduced by lowering up to an average of six times of the seed set size. Results showed that selecting the top influencers using a combination of metrics is effective in minimizing DC and maximizing contagion up to 77% and 32% respectively. The proposed IDTU has been able to maximize information diffusion while minimizing DC. It demonstrates a more balanced and nuanced approach regarding influencer selection. This will be useful for business and social media marketers in leveraging their promotional activities. 2020 Thesis https://etd.uum.edu.my/8398/ https://etd.uum.edu.my/8398/1/s901087_01.pdf text eng public https://etd.uum.edu.my/8398/2/s901087_02.pdf text eng public https://etd.uum.edu.my/8398/3/s901087%20references.docx text aa public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng aa
advisor	Ahmad, Rahayu
topic	T58.6-58.62 Management information systems
spellingShingle	T58.6-58.62 Management information systems Temitope, Olanrewaju Abdus-Samad Influence maximisation towards target users and minimal diffusion of information based on information needs
description	Influence maximisation within social network is essential to the modern business. Influence Maximisation Problem (IMP) involves the minimal selection of influencers that leads to maximum contagion while minimizing Diffusion Cost (DC). Previous models of IMP do not consider DC in spreading information towards target users. Furthermore, influencer selection for varying information needs was not considered which leads to influence overlaps and elimination of weak nodes. This study proposes the Information Diffusion towards Target Users (IDTU) algorithm to enhance influencer selection while minimizing the DC. IDTU was developed on greedy approach by using graph sketches to improve the selection of influencers that maximize influence spread to a set of target users. Moreover, the influencer identification based on specific needs was implemented using a General Additive Model on four fundamental centralities. Experimental method was used by employing five social network datasets including Epinions, Wiki-Vote, SlashDot, Facebook and Twitter from Stanford data repository. Evaluation on IDTU was performed against 3 greedy and 6 heuristics benchmark algorithms. IDTU identified all the specified target nodes while lowering the DC by up to 79%. In addition, the influence overlap problem was reduced by lowering up to an average of six times of the seed set size. Results showed that selecting the top influencers using a combination of metrics is effective in minimizing DC and maximizing contagion up to 77% and 32% respectively. The proposed IDTU has been able to maximize information diffusion while minimizing DC. It demonstrates a more balanced and nuanced approach regarding influencer selection. This will be useful for business and social media marketers in leveraging their promotional activities.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Temitope, Olanrewaju Abdus-Samad
author_facet	Temitope, Olanrewaju Abdus-Samad
author_sort	Temitope, Olanrewaju Abdus-Samad
title	Influence maximisation towards target users and minimal diffusion of information based on information needs
title_short	Influence maximisation towards target users and minimal diffusion of information based on information needs
title_full	Influence maximisation towards target users and minimal diffusion of information based on information needs
title_fullStr	Influence maximisation towards target users and minimal diffusion of information based on information needs
title_full_unstemmed	Influence maximisation towards target users and minimal diffusion of information based on information needs
title_sort	influence maximisation towards target users and minimal diffusion of information based on information needs
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2020
url	https://etd.uum.edu.my/8398/1/s901087_01.pdf https://etd.uum.edu.my/8398/2/s901087_02.pdf https://etd.uum.edu.my/8398/3/s901087%20references.docx
_version_	1747828385612038144

Influence maximisation towards target users and minimal diffusion of information based on information needs

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