中国科学院大学学报2024，Vol.41Issue(1)：136-144,9.DOI:10.7523/j.ucas.2022.057
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
摘要
Abstract
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Key words
online social network/rumor/identify core nodes/GCN分类
信息技术与安全科学引用本文复制引用
李元,张栖,朱建明,焦建彬..基于多层次深度模型的社交网络核心谣言传播节点识别[J].中国科学院大学学报,2024,41(1):136-144,9.基金项目
国家自然科学基金(72074203)资助 （72074203）
