基于话题概率模型的语义社区发现方法研究

辛宇; 谢志强; 杨静

doi:10.16383/j.aas.2015.c150136

基于话题概率模型的语义社区发现方法研究

doi: 10.16383/j.aas.2015.c150136

辛宇^1,2,
谢志强¹,
杨静^2, ,

1.
哈尔滨理工大学计算机科学与技术学院哈尔滨 150080;
2.
哈尔滨工程大学计算机科学与技术学院哈尔滨 150001

基金项目:

国家自然科学基金(61370083, 61370086), 国家教育部博士点基金(20122304110012), 黑龙江省自然科学基金(F201101), 黑龙江省教育厅科技项目(12531105), 黑龙江省博士后科研启动项目(LBH-Q13092)资助

详细信息

作者简介:
辛宇哈尔滨理工大学计算机科学与技术学院讲师. 2015 年获得哈尔滨工程大学计算机应用技术博士学位. 主要研究方向为数据库与知识工程. E-mail: xinyu@hrbeu.edu.cn

通讯作者:
杨静哈尔滨工程大学计算机科学与技术学院教授. 主要研究方向为数据库与知识工程. 本文通信作者. E-mail: yangjing@hrbeu.edu.cn

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出版历程
- 收稿日期: 2015-03-31
- 修回日期: 2015-06-24
- 刊出日期: 2015-10-20

Semantic Community Detection Research Based on Topic Probability Models

XIN Yu^1,2,
XIE Zhi-Qiang¹,
YANG Jing^{2
, ,}

1.
College of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080;
2.
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001

Funds:

Supported by National Natural Science Foundation of China (61370083, 61370086), National Research Foundation for the Doctoral Program of Higher Education of China (20122304110012), Natural Science Foundation of Heilongjiang Province (F201101), Educational Commission of Heilongjiang Province (12531105), Postdoctoral Science Foundation of Heilongjiang Province (LBH-Q13092)

摘要

摘要: 语义社会网络(Semantic social network, SSN)是一种由信息节点及社会关系构成的复杂网络, 也是语义信息时代社会网络技术研究的热点, 相较于传统社会网络更具实用价值. 其研究内容包含了社会网络的语义分析及社会关系分析, 因此, 语义社会网络的社区挖掘建模具有一定的复杂性. 在语义社会网络的社区挖掘研究方面, 本文分析了当前基于话题概率模型的语义社区发现方法, 并在综述其内容的同时总结了各方法的优缺点, 为后续研究提供了理论基础. 在语义社会网络社区挖掘结果的评判方面, 本文归纳了相关的评价模型, 并通过实验分析对比了各模型对拓扑相关性和语义相关性的倾向性.
- 语义社会网络 /
- 话题概率模型 /
- 社区挖掘 /
- 社区结构
Abstract: Semantic social network (SSN) is a complex network consisting of textual node information and social relationships, and has more valuable applications than the traditional social network which focuses on social network analysis (SNA) with semantic information. As it contains the semantic analysis and social relationship analysis, there is some complexity for the modeling in mining the SNA community. In the SNA community mining aspect, the advantages and disadvantages of each method based on topic probability models are summarized to provide a theoretical basis for the further research. In the evaluation aspect of SNA community mining, relevant evaluation models are summarized. The tendency of each evaluation model toward topological and semantic relevance is compared by experimental analysis.
- Semantic social network (SSN) /
- topic probability model /
- community mining /
- community structure

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参考文献(99)

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基于话题概率模型的语义社区发现方法研究

doi: 10.16383/j.aas.2015.c150136

作者简介:
辛宇哈尔滨理工大学计算机科学与技术学院讲师. 2015 年获得哈尔滨工程大学计算机应用技术博士学位. 主要研究方向为数据库与知识工程. E-mail: xinyu@hrbeu.edu.cn

通讯作者:
杨静哈尔滨工程大学计算机科学与技术学院教授. 主要研究方向为数据库与知识工程. 本文通信作者. E-mail: yangjing@hrbeu.edu.cn

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Semantic Community Detection Research Based on Topic Probability Models

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基于话题概率模型的语义社区发现方法研究

doi: 10.16383/j.aas.2015.c150136

作者简介: 辛宇 哈尔滨理工大学计算机科学与 技术学院讲师. 2015 年获得哈尔滨工程 大学计算机应用技术博士学位. 主要研 究方向为数据库与知识工程. E-mail: xinyu@hrbeu.edu.cn

通讯作者: 杨静 哈尔滨工程大学计算机科学与 技术学院教授. 主要研究方向为数据库 与知识工程. 本文通信作者. E-mail: yangjing@hrbeu.edu.cn

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Semantic Community Detection Research Based on Topic Probability Models

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作者简介:
辛宇哈尔滨理工大学计算机科学与技术学院讲师. 2015 年获得哈尔滨工程大学计算机应用技术博士学位. 主要研究方向为数据库与知识工程. E-mail: xinyu@hrbeu.edu.cn

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杨静哈尔滨工程大学计算机科学与技术学院教授. 主要研究方向为数据库与知识工程. 本文通信作者. E-mail: yangjing@hrbeu.edu.cn