Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction
With society entering the Internet era, the volume and speed of data and information have been increasing. Predicting the popularity of information cascades can help with high-value information delivery and public opinion monitoring on the internet platforms. The current state-of-the-art models for predicting information popularity utilize deep learning methods such as graph convolution networks (GCNs) and recurrent neural networks (RNNs) to capture early cascades and temporal features to predict their popularity increments. However, these previous methods mainly focus on the micro features of information cascades, neglecting their general macroscopic patterns. Furthermore, they also lack...
| Authors | Guangyin Jin, Xiaohan Ni, Yanjie Song et al. |
|---|---|
| Primary category | cs.SI |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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