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CST-Net: community-guided structural-temporal convolutional networks for popularity prediction.

Xuxu Zheng^1,2, Peng Bao³, Lin Qi³

¹University of Chinese Academy of Sciences, Beijing, China.

Peerj. Computer Science

|September 24, 2025

Summary

This summary is machine-generated.

Predicting online content popularity is crucial. A new deep learning framework, CST-Net, effectively forecasts content popularity by analyzing user communities and information cascades, outperforming existing methods.

Keywords:

Information diffusion Neural networks Popularity prediction Social network

Related Experiment Videos

Area of Science:

Computational Social Science
Machine Learning
Network Science

Background:

Predicting online content popularity is vital across various domains.
Challenges include popularity inequality and complex influencing factors.
Existing methods (feature-driven, generative, deep learning) have limitations.

Purpose of the Study:

To introduce CST-Net, an end-to-end deep learning framework for improved popularity prediction.
To address the shortcomings of current popularity prediction methodologies.

Main Methods:

Learned low-dimensional user embeddings from historical interactions.
Clustered users into communities and represented information cascades as community interaction matrices.
Applied a convolutional architecture to extract cascade representations.
Combined structural and temporal features for incremental popularity prediction.

Main Results:

CST-Net demonstrated superior performance on microblogging and academic citation datasets.
The model consistently outperformed existing competitive popularity prediction methods.
Validation on population-scale datasets confirmed effectiveness.

Conclusions:

CST-Net offers a robust and effective approach to predicting online content popularity.
The framework's ability to capture complex cascade dynamics is key to its success.
This work advances the field of computational social science and predictive modeling.