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

作　　者：李元 张栖 朱建明 焦建彬 LI Yuan;ZHANG Qi;ZHU Jianming;JIAO Jianbin(School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;School of Emergency Management Science and Engineering,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院大学电子电气与通信工程学院,北京100049 [2]中国科学院大学应急管理科学与工程学院,北京100049

出　　处：《中国科学院大学学报（中英文）》2024年第1期136-144,共9页Journal of University of Chinese Academy of Sciences

基　　金：国家自然科学基金(72074203)资助。

摘　　要：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.

关 键 词：在线社交网络 谣言 识别核心节点 图卷积神经网络 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]