MMCasN: Macroscopic and microscopic properties fusion for predicting information cascades

Information cascade plays an important role in today’s social networks and complex systems. Predicting the growth of cascades can help people understand and intervene in cascading processes. Existing methods for cascade prediction mainly focus on extracting local node features, ignoring the dynamics of cascades and the macroscopic characteristics of the overall cascade structure. In this paper, we propose a novel cascade prediction method named MMCasN (a Fusion Macroscopic and Microscopic Properties for Predicting Information Cascades Network) by combining micro-level features with macro-level features. Firstly, the information cascade graph can be divided into multiple snapshots based on the predefined time window. Then, for each snapshot, micro-level features are extracted using GraphSage to capture node-level characteristics, while macro-level features handle uncertain cascade sizes using a mapping approach. Furthermore, time features are extracted using Bi-LSTM, and a multi-layer perceptron is employed for prediction. Experimental results demonstrate that MMCasN outperforms other baseline methods on two real-world datasets, exhibiting more superior prediction performance.