Journal Article

CasDacGCN: A Dynamic Attention-Calibrated Graph Convolutional Network for Information Popularity Prediction

Liao, Kaili, Zhang, Zhirong, Niu, Sen, Zhang, Bofeng, Li, Haiyan, Li, Bingchun, Zhu, Yanlin

2025

Overview

Information popularity prediction is a critical problem in social network analysis. With the increasing prevalence of social platforms, accurate prediction of the diffusion process has become increasingly important. Existing methods mainly rely on graph neural networks to model structural relationships, but they are often insufficient in capturing the complex interplay between temporal evolution and local cascade structures, especially in real-world scenarios involving sparse or rapidly changing cascades. To address this issue, we propose the Cascading Dynamic attention-calibrated Graph Convolutional Network, named CasDacGCN. It enhances prediction performance through spatiotemporal feature fusion and adaptive representation learning. The model integrates snapshot-level local encoding, global temporal modeling, cross-attention mechanisms, and a hypernetwork-based sample-wise calibration strategy, enabling flexible modeling of multi-scale diffusion patterns. Results from experiments demonstrate that the proposed model consistently surpasses existing approaches on two real-world datasets, validating its effectiveness in popularity prediction tasks.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Analysis / Artificial neural networks / Attention / Calibration / Convolutional codes / Deep learning / Entropy / Forecasts and trends / graph convolutional network / Graph neural networks / Graph theory / Graphs / information diffusion / information popularity prediction / Information theory / Machine learning / Mathematical research / Modelling / Network analysis / Popularity / Prediction theory / Propagation / Social networks / temporal graph / Trends