基于多层次深度模型的社交网络核心谣言传播节点识别

Identification of core rumor spreaders in online social networks based on multi-stage deep model

鉴于在线社交网络谣言控制的时效性与及时性,提出一种基于多维特征的两阶段图卷积网络(MSF-GCN)学习模型。该模型试图在尽早的时间实现精准定位核心谣言传播节点,从源头上阻断谣言扩散。通过将MSF-GCN方法与其他基准方法在谣言数据集上进行比对,实验结果验证了MSF-GCN学习模型更加有效。

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.