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山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (2): 51-60.doi: 10.6040/j.issn.1672-3961.0.2022.157

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刻画多种潜在关系的泊松-伽马主题模型

吴艳丽,刘淑薇,何东晓,王晓宝*,金弟   

  1. 天津大学智能与计算学部, 天津 300350
  • 收稿日期:2022-04-19 出版日期:2023-04-22 发布日期:2023-04-21
  • 作者简介:吴艳丽(1996— ),女,河北廊坊三河人,硕士研究生,主要研究方向包括社团发现和社交网络分析. E-mail:wuyanli_098@tju.edu.cn. *通信作者简介:王晓宝(1992— ),男,浙江金华人,博士,主要研究方向为社区检测、社会网络分析和机器学习. E-mail:wxbxmt@tju.edu.cn
  • 基金资助:
    国家自然科学基金面上项目(61876128)

Poisson-gamma topic model of describing multiple underlying relationships

WU Yanli, LIU Shuwei, HE Dongxiao, WANG Xiaobao*, JIN Di   

  1. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
  • Received:2022-04-19 Online:2023-04-22 Published:2023-04-21

摘要: 为探索节点间链接结构的多种潜在关系并对其进行语义解释,提出一个刻画多种潜在关系的泊松-伽马主题模型,刻画不同潜在关系下节点内容与链接结构(边)的生成过程,利用全期望定律来聚合所有潜在关系中的内容信息与拓扑信息。对于模型推断,进一步提出一种封闭式的吉布斯采样算法。在8个真实数据集上与8种代表性社团发现方法进行比较,并对所有潜在关系中的链接结构进行可视化和案例分析。试验结果表明,本研究方法优于8种代表性的社团发现方法,能够在多种潜在关系中探索节点间链接结构的有效性,还能够利用节点内容来解释链接关系中的语义信息。

关键词: 社交网络, 社团发现, 概率图模型, 主题模型, 语义

中图分类号: 

  • TP391
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