Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Influential Node Analysis In Social Network

Authors
G. Gokulnath1, J. Kanishk1, I. Hareesh1, *, D. Punitha1
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamilnadu, India
*Corresponding author. Email: hareeshilangovan1@gmail.com
Corresponding Author
I. Hareesh
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_75How to use a DOI?
Keywords
Social Network; Influential Node; Truthfulness Algorithm; Authentic Engagement; Targeted Advertising
Abstract

In digital environments, social networks are profoundly shaped by the actions of influential nodes, such as celebrities, content creators, and highly engaged users. Their interactions—liking, sharing, commenting, visiting pages, and purchasing products—serve as catalysts for engagement cascades that influence the broader user base. This study introduces a Truthfulness Algorithm designed to distinguish between authentic and inauthentic behaviors of influential nodes, using a Hidden Markov Model to simulate behavior transitions over time. Through simulations, we find that authentic behaviors by influential nodes significantly enhance user engagement and improve engagement sustainability compared to inauthentic behaviors. Additionally, the model helps advertisers improve targeting accuracy by prioritizing genuinely influential nodes, while regular users experience more relevant and trustworthy interactions. These findings highlight the importance of fostering authentic engagement to optimize both marketing efficiency and user satisfaction. The study provides valuable insights for creating more effective targeted advertising and social media marketing strategies. The study aims to improve the specific targeted engagements to achieve more than 35% of user engagements and increased revenue generation using ads.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_75How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - G. Gokulnath
AU  - J. Kanishk
AU  - I. Hareesh
AU  - D. Punitha
PY  - 2025
DA  - 2025/10/31
TI  - Influential Node Analysis In Social Network
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 925
EP  - 941
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_75
DO  - 10.2991/978-94-6463-866-0_75
ID  - Gokulnath2025
ER  -