文献类型：期刊文献

英文题名：Macroscopic diffusion prediction in social networks based on spatio-temporal and trend features

作者：Zhang, Xueqin[1];Lu, Yisong[1];Liu, Gang[1];Chen, Xiaowei[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Inst Technol, Sch Comp Sci & Informat Engn, Shanghai 201418, Peoples R China

年份：2025

卷号：244

外文期刊名：COMPUTER COMMUNICATIONS

收录：;EI(收录号：20254519455500);WOS:【SCI-EXPANDED(收录号:WOS:001613798100001)】；

基金：Acknowledgments This work was supported by the Major Program of National Fund Philosophy and Social Science of China (grant number: 23&ZD142) .

语种：英文

外文关键词：Social network; Macroscopic diffusion prediction; Cascade graph; Graph attention network; Sparse matrix factorization

摘要：Predicting the scale of information diffusion in social networks can sense the future propagation of information advance, which plays a crucial role in controlling the diffusion of harmful information. We propose STTFP (Spatio-Temporal and Trend Features for Prediction), a deep learning framework that integrates temporal, spatial, and trend features to improve macroscopic diffusion prediction accuracy. This framework first utilizes graph attention networks to extract node interaction features from cascaded graphs. It captures node position features from diffusion sequences, and uses sparse matrix factorization to extract node features from social network graphs. Then it adopts bi-directional gated recurrent units and self-attention mechanisms to deeply mine spatio-temporal features. Additionally, we design an attention-based convolutional neural network to capture the short-term fluctuations in the information propagation process, while long short-term memory networks are used to uncover historical forwarding variation in diffusion scales. By fusing these features, the framework achieves incremental predictions of information diffusion. Experiments on three public datasets show that our method effectively enhances the accuracy of macroscopic diffusion predictions.