电讯技术2025，Vol.65Issue(5)：684-692,9.DOI:10.20079/j.issn.1001-893x.240802001
不完备观测数据条件下的消息传播扩散建模与预测方法
Modeling and Predicting Message Propagation Diffusion under Incomplete Observational Data
摘要
Abstract
To address the challenges posed by malicious information and incomplete observation data during the early stages of rumor propagation on social networks,a novel approach for message diffusion modeling is proposed under incomplete data conditions.The method aims to overcome the limitations of traditional approaches,which struggle to accurately represent the full state of information diffusion in social networks,especially when cascade information is incomplete due to data timeliness.To achieve this,the network structural features and inter-node influence relationships are combined to construct user feature input vectors,and graph neural network(GNN)is used to aggregate features from neighboring nodes,thereby capturing complex node relationships and global network structure.Furthermore,by incorporating a diffusion propagation dynamics model,the method iteratively propagates the estimated states of nodes to their neighbors,reconstructing a more complete diffusion cascade.Experimental results demonstrate that,even under incomplete observation data,this method can effectively simulate information diffusion,achieving a cascade completeness of up to 88.79％,which provides an efficient and feasible new approach for research in related fields.关键词
社交网络/消息源检测/消息扩散建模/信息级联Key words
social network/source detection/diffusion modeling/information cascade分类
计算机与自动化引用本文复制引用
贾莹,黄志成,李佳斌,卢梦萍,焦凯玉,胡航宇..不完备观测数据条件下的消息传播扩散建模与预测方法[J].电讯技术,2025,65(5):684-692,9.基金项目
国家自然科学基金青年基金项目(62101095) （62101095）
