Abstract
Online Social Networks (OSNs) such as Twitter, Sina Weibo, and Facebook play an important role in our daily life recently. The influence diffusion between users is a common phenomenon on OSNs, which has been applied in numerous applications such as rumor detection and product marketing. Most of the existing influence modeling methods are based on complete data. However, due to certain reasons like privacy protection, it is very hard to obtain complete history data in OSNs. In this paper, we propose a new method to estimate user influence based on incomplete data from user behaviors. Firstly, we apply the maximum likelihood estimator to estimate the user’s missing behaviors. Then, we use direct interaction to get the influence of the sender and receiver. In addition, we apply different actions between users to improve the performance of our method. Empirical experiments on the Weibo dataset show that our method outperforms the existing methods.
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Acknowledgments
This work was supported by Shanghai Science and Technology Commission (No. 22YF1401100), Fundamental Research Funds for the Central Universities (No. 22D111210, 22D111207), and National Science Fund for Young Scholars (No. 62202095).
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Huang, W., Sun, G., Wang, M., Zhao, W., Yang, J. (2023). Influence Embedding from Incomplete Observations in Sina Weibo. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_9
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DOI: https://doi.org/10.1007/978-981-99-7254-8_9
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