Identification of core rumor spreaders in online social networks based on multi-stage deep model
Online social networks have become the disaster areas where rumors grow.It is of great significance to identify core rumor spreaders for rumor prevention and control.The traditional rumor control model is mainly based on the dynamics of rumor propagation,and it is mainly focused on in-event or post-event control.In view of the timeliness of rumor control,this paper proposes a multi-stage graph convolutional network based on multi-dimensional features(MSF-GCN)deep learning model to accurately locate core rumor spreaders as early as possible and block rumor diffusion from the source.This work compares the MSF-GCN method with other three baseline methods on rumor data set,and the experimental results verify that our method is more efficient.
国家科技期刊平台
NSTL
万方数据
维普
