计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (3): 88-96.doi: 10.11896/jsjkx.250800013

• 基于AGI技术的智能信息系统 • 上一篇    下一篇

融合传播结构的群体语义驱动超图网络虚假信息检测方法

崔梦天1,2, 何俐汶2, 谢琪2, 王方2   

  1. 1 西南民族大学国家安全与区域发展研究院 成都 610041
    2 西南民族大学计算机与人工智能学院 成都 610041
  • 收稿日期:2025-08-04 修回日期:2025-10-30 发布日期:2026-03-12
  • 通讯作者: 王方(wangfang@swun.edu.cn)
  • 作者简介:(mengtian.cui@swun.edu.cn)
  • 基金资助:
    国家自然科学基金(12050410248);国家留学基金委(202508510176);科技部高端外国专家引进计划项目(H20240672);科技创新平台能力建设项目:人工智能前沿技术研究(ZYN2025258)

Group Semantic-driven Hypergraph Network for Disinformation Detection with Fusion PropagationStructure

CUI Mengtian1,2, HE Liwen2, XIE Qi2, WANG Fang2   

  1. 1 National Security and Regional Development Institute, Southwest Minzu University, Chengdu 610041, China
    2 College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu 610041, China
  • Received:2025-08-04 Revised:2025-10-30 Online:2026-03-12
  • About author:CUI Mengtian,born in 1972,Ph.D,professor,is a member of CCF(No.W5135M).Her main research interests include information security,disinformation detection,and intelligent software engineering.
    WANG Fang,born in 1976,Ph.D,senior laboratory technician,is a member of CCF(No.X9934M).His main research interests include intelligent optimization algorithm,swarm intelligence,spatial information processing and parallel computing.
  • Supported by:
    National Natural Science Foundation of China(12050410248),China Scholarship Council(202508510176),Foreign Talents Program of the Ministry of Human Resources and Social Security of China(H20240672) and Technological Innovation Platform Capacity Building Project:Artificial Intelligence Frontier Technology Research(ZYN2025258).

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摘要： 在高频交互的社交网络环境中,虚假信息常通过用户群体的协同扩散来迅速传播,呈现出复杂的多阶传播结构和语义关联,是国家安全技术领域亟待应对的关键挑战之一。然而,现有仅依赖文本内容或传统传播图结构的检测方法无法有效建模这种高阶语义交互与协同行为。为此,提出一种融合传播结构的群体语义驱动超图网络方法(GSHN-DD)。该方法首先基于用户行为与信息主题构建初始超图,以捕捉群体协同与语义关联;然后通过链路预测与双层筛选机制挖掘潜在高阶超边,构建增强型超图拓扑结构;在此基础上,采用超图卷积网络与双层注意力机制,实现对全局群体传播模式与局部关键超边特征的融合;最后将传播特征与超图语义特征融合,生成统一的嵌入表示,并将其输入全连接分类器,完成虚假信息识别。在PolitiFact和GossipCop数据集上进行了实验,结果表明,GSHN-DD相较于最优基线方法,准确率提升了2~5个百分点,F1值提升了2~7个百分点。

关键词: 虚假信息检测, 群体语义超图, 链路预测, 高阶超边建模, 超图网络

Abstract: In social networks characterized by frequent and intensive user interactions,disinformation tends to propagate rapidly through collaborative diffusion,exhibiting complex multi-level propagation structures and semantic associations.This represents one of the critical challenges urgently needing to be addressed in the field of national security technology.However,current detection methods,limited to either textual content or conventional propagation graphs,fail to capture these high-order semantic interactions and collaborative behaviors.Therefore,this paper proposes a group-semantics-driven hypergraph network method(GSHN-DD) that integrates propagation structures.The proposed method first constructs an initial hypergraph based on user behaviors and information topics to capture group-level coordination and semantic associations.Subsequently,latent higher-order hyperedges are mined through link prediction combined with a dual-layer filtering mechanism,resulting in an enhanced hypergraph topology.Building on this foundation,a hypergraph convolutional network,combined with a dual-layer attention mechanism,is utilized to integrate global group propagation patterns and local key hyperedge features.Finally,the model integrates propagation features and hypergraph semantic representations to generate unified embeddings,which are fed into a fully connected classifier for disinformation detection.Experimental results on the PolitiFact and GossipCop datasets demonstrate that GSHN-DD performs better than the baseline methods,achieving 2 to 5 percentage point improvement in accuracy and 2 to 7 percentage point increase in F1-score.

Key words: Disinformation detection, Group semantic hypergraph, Link prediction, High-order hyperedge modeling, Hypergraph network

中图分类号: 

  • TP391
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