Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xinyu Li, Jinyang Huang, Xiang Zhang, Peng Zhao, Meng Wang
| Summary:
Describing the dynamics of information dissemination within social networks
poses a formidable challenge. Despite multiple endeavors aimed at addressing
this issue, only a limited number of studies have effectively replicated and
forecasted the evolving course of information dissemination. In this paper, we
propose a novel model, DM-NAI, which not only considers the information
transfer between adjacent users but also takes into account the information
transfer between non-adjacent users to comprehensively depict the information
dissemination process. Extensive experiments are conducted on six datasets to
predict the information dissemination range and the dissemination trend of the
social network. The experimental results demonstrate an average prediction
accuracy range of 94.62% to 96.71%, respectively, significantly outperforming
state-of-the-art solutions. This finding illustrates that considering
information transmission between non-adjacent users helps DM-NAI achieve more
accurate information dissemination predictions.
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