Local-Forest Method for Superspreaders Identification in Online Social Networks

Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation m...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2022-09, Vol.24 (9), p.1279
Main Authors: Hao, Yajing, Tang, Shaoting, Liu, Longzhao, Zheng, Hongwei, Wang, Xin, Zheng, Zhiming
Format: Article
Language:English
Subjects:
Algorithms
Analysis
China
complex network
Epidemics
Identification
Identification methods
influential nodes
Information dissemination
Methods
online social network
Online social networks
Performance evaluation
Propagation
Social networks
spreading
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Summary:Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we first adopt the Push-Republish (PR) model as the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly decreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.
ISSN:1099-4300
1099-4300
DOI:10.3390/e24091279