自动化学报Issue(2)：362-375,14.DOI:10.16383/j.aas.2015.c140136
一种面向语义重叠社区发现的Blo ck场取样算法
An Overlapping Community Structure Detecting Algorithm in Semantic Social Network Based on Block Field
摘要
Abstract
The semantic social network (SSN) is a new kind of complex networks consisting of the node content and topological relationship. The traditional community detection algorithms need to preset the number of the communities and could not detect the overlapping communities. To solve this problem, an overlapping community structure detecting algorithm in semantic social network based on the block field is proposed. Firstly, it takes the latent dirichlet allocation (LDA) model as the semantic analyzing model, establishing the block-author-topic (BAT) model with the sampling node as the core node. Secondly, it suggests the measurement of the semantic cohesion of the block field, depending on the analysis of SSN, to achieve the evaluation of semantic information. Finally, it improves the label propagation algorithm (LPA) which could detect the overlapping communities, with the semantic cohesion as input, and designs the S Q measurement modularity for semantic measuring. The effciency and feasibility of the algorithm and the semantic modularity are verified via experimental analysis.关键词
语义社会网络/重叠社区/LDA模型/社区发现Key words
Semantic social network/overlapping community/latent dirichlet allocation (LDA)/community detection引用本文复制引用
辛宇,杨静,谢志强..一种面向语义重叠社区发现的Blo ck场取样算法[J].自动化学报,2015,(2):362-375,14.基金项目
国家自然科学基金(61370083,61073043,61073041,61370086),国家教育部博士点基金(20112304110011,20122304110012)资助@@@@Supported by National Natural Science Foundation of China (61370083,61073043,61073041,61370086) and National Re-search Foundation for the Doctoral Program of Higher Educa-tion of China (20112304110011,20122304110012) （61370083,61073043,61073041,61370086）
