Next Article in Journal
Numerical Investigation of Micromechanical Failure Evolution in Rocky High Slopes Under Multistage Excavation
Previous Article in Journal
Off-the-Shelf Masked Ultrasonic Atomization for Hydrophilic Droplet Microarrays and Gradient Screening
Previous Article in Special Issue
Schema Retrieval with Embeddings and Vector Stores Using Retrieval-Augmented Generation and LLM-Based SQL Query Generation
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Finding Influencers Based on Social Interaction and Graph Structure in Social Media

1
Department of Bigdata, Chungbuk National University, Cheongju 28644, Republic of Korea
2
Department of Artificial Intelligence Convergence, Wonkwang University, Iksan 54538, Republic of Korea
3
School of Information & Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(2), 738; https://doi.org/10.3390/app16020738 (registering DOI)
Submission received: 29 November 2025 / Revised: 6 January 2026 / Accepted: 9 January 2026 / Published: 10 January 2026
(This article belongs to the Special Issue AI-Based Data Science and Database Systems)

Abstract

With the development of online social media, influencer detection methods on these platforms have become an important area of study. However, existing influencer detection methods often place significant emphasis on the number of followers, which can lead to a drawback in maintaining the influence of users who have not been very active recently. In this paper, we propose an influencer detection method that takes both social interactions and the graph structure of social media into account. By considering both social interactions and graph structure, the proposed method prevents influence scores of users who have not been recently active from remaining disproportionately high. To demonstrate the superiority of the proposed method, we conducted a performance comparison with existing methods.
Keywords: social media; social interaction; graph structure; influencer detection; page rank social media; social interaction; graph structure; influencer detection; page rank

Share and Cite

MDPI and ACS Style

Lim, J.; Choi, H.; Choi, S.; Bok, K.; Yoo, J. Finding Influencers Based on Social Interaction and Graph Structure in Social Media. Appl. Sci. 2026, 16, 738. https://doi.org/10.3390/app16020738

AMA Style

Lim J, Choi H, Choi S, Bok K, Yoo J. Finding Influencers Based on Social Interaction and Graph Structure in Social Media. Applied Sciences. 2026; 16(2):738. https://doi.org/10.3390/app16020738

Chicago/Turabian Style

Lim, Jongtae, Hwanyong Choi, Sanghyun Choi, Kyoungsoo Bok, and Jaesoo Yoo. 2026. "Finding Influencers Based on Social Interaction and Graph Structure in Social Media" Applied Sciences 16, no. 2: 738. https://doi.org/10.3390/app16020738

APA Style

Lim, J., Choi, H., Choi, S., Bok, K., & Yoo, J. (2026). Finding Influencers Based on Social Interaction and Graph Structure in Social Media. Applied Sciences, 16(2), 738. https://doi.org/10.3390/app16020738

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop