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一种面向语义重叠社区发现的Link-Block算法
引用本文:辛宇,杨静,谢志强.一种面向语义重叠社区发现的Link-Block算法[J].软件学报,2016,27(2):363-380.
作者姓名:辛宇  杨静  谢志强
作者单位:哈尔滨工程大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001;哈尔滨理工大学 计算机科学与技术学院, 黑龙江 哈尔滨 150080,哈尔滨工程大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001,哈尔滨理工大学 计算机科学与技术学院, 黑龙江 哈尔滨 150080
基金项目:国家自然科学基金(61370083, 61370086); 教育部博士点基金(20122304110012); 黑龙江省博士后基金(LBH-Z1509 6); 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)
摘    要:语义社会网络是一种由信息节点及社会关系构成的新型复杂网络,传统语义社会网络分析算法在进行社区挖掘时需要预先设定社区个数,且无法发现重叠社区.针对这一问题,提出一种面向语义社区发现的link-block算法.该算法首先以LDA模型为语义信息模型,创新性地建立了以link为核心的block区域LBT(link-block-topic)取样模型;其次,根据link-block语义分析结果,建立可度量link-block区域的语义链接权重方法,实现了语义信息的可度量化;最后,根据语义链接权重建立了以link-block为单位的聚类算法以及可评价语义社区的SQ模型,并通过实验分析,验证了该算法及SQ模型的有效性及可行性.

关 键 词:语义社会网络  重叠社区  语义社区  LDA  link-block
收稿时间:2014/4/20 0:00:00
修稿时间:2014/11/17 0:00:00

Link-Block Method for the Semantic Overlapping Community Detection
XIN Yu,YANG Jing and XIE Zhi-Qiang.Link-Block Method for the Semantic Overlapping Community Detection[J].Journal of Software,2016,27(2):363-380.
Authors:XIN Yu  YANG Jing and XIE Zhi-Qiang
Affiliation:College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;College of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China,College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China and College of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
Abstract:Since the semantic social network (SSN) is a new kind of complex networks, the traditional community detection algorithms which require presetting the number of the communities, cannot detect the overlapping communities. To solve this problem, an overlapping community structure detecting algorithm in semantic social networks based on the link-block is proposed. First, the measurement of the semantic weight of links for the link-block is established depending on the analysis of LBT. Secondly, a method to measure the semantic links weight of link-block area is developed to provide the measurement of semantic information. Thirdly, the overlapping community detection cluster method is designed, based on the semantic weight of links, with the link-block as the element. Finally, the SQ modularity for the measurement of semantic communities is obtained. The efficiency and feasibility of the algorithm and the semantic modularity are verified by experimental analysis.
Keywords:semantic social network  overlapping community  semantic community  LDA  link-block
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