Abstract
This paper introduces an early content propagation popularity prediction model based on graph neural network and variational inference topic dependent dynamic variational autoencoder model (CD-VAE). CD-VAE captures the dynamics in the content propagation process, aggregates the topological information in the information diffusion process using GraphSAGE, approaches the uncertainty in terms of time and node from the perspective of probability by introducing two variational autoencoders, considers the changes in semantic characteristics in the process by integrating natural language processing methods into the model, and therefore significantly improves its prediction performance.
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Acknowledgement
This work was supported by the Open Funding Projects of the State Key Laboratory of Communication Content Cognition (No. 20K05 and No. A02107).
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Tong, C. et al. (2023). Topic-Aware Model for Early Cascade Population Prediction. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_47
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DOI: https://doi.org/10.1007/978-3-031-28124-2_47
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