Information Dissemination Model Based on Social Networks Characteristics

Jianwei Ding, Zehan Li, Xia Wu, Rong Liu and Hangyu Hu ()
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Jianwei Ding: 30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610041, China
Zehan Li: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Xia Wu: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Rong Liu: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Hangyu Hu: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Mathematics, 2025, vol. 13, issue 8, 1-17

Abstract: As a crucial platform, online social networks provide individuals with avenues to exchange ideas and access information, exerting profound impacts on society and nations. In social networks, key users, serving as edge nodes in the process of information dissemination, play a pivotal role because they directly connect users and can process and forward information in real-time. Furthermore, edge nodes enable personalized information dissemination based on users’ social relationships and behavioral characteristics, more accurately reflecting the pathways and influence of information spread. Early research primarily focused on the dynamics of information dissemination in complex networks, aiming to develop general predictive models to understand the overall mechanisms of information spread. However, there is still a lack of research on how the unique social relationships and attributes in social networks affect information dissemination. To address this gap, we conducted an in-depth study of the characteristics of information dissemination in social networks and improved the classic independent cascade model, proposing a novel predictive model for information spread. This enhancement not only improves the accuracy of simulating the information dissemination process in social networks but also demonstrates that our proposed model significantly outperforms other models in terms of accuracy. The findings provide a more effective tool for understanding and predicting information dissemination in social networks.

Keywords: social network; information diffusion; complex networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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