Abstract: Information cascade popularity prediction is critical for understanding the dynamics of information dissemination and mitigating the spread of misinformation.Although existing deep learning methods have achieved improvements in predictive accuracy,they still exhibit limitations in disentangling confounding factors and uncovering the deep causal relationships between cascade structures and popularity,which undermines both the reliability and interpretability of predictions.To address these challenges,this paper proposes a novel cascade popularity prediction model based on causal subgraph learning,named causal-aware cascade model(CauCas).CauCas introduces graph data augmentation to impose interventions on the original cascade graphs and encodes multi-level cascade representations for both the original and augmented graphs.Through specialized layer-wise feature selection and weighted fusion strategies,the model derives graph-level representations for each graph,and leverages adaptive instance normalization to learn robust features that are less sensitive to interventions and more likely to reflect causal relationships.Finally,the fused feature representations are fed into a multilayer perceptron to perform popularity prediction.Experimental results on Twitter,Weibo,and APS three public datasets demonstrate that CauCas achieves superior performance,consistently outperforming state-of-the-art methods across diverse datasets and prediction windows.

Key words: Causal subgraph, Information cascade, Popularity prediction, Causal inference, Graph neural networks, Attention mechanism