摘要
【目的/意义】基于标签系统所蕴含的语义信息与隐性社会网络,构建融合标签概念空间及用户网络的语义社团发现模型,提高社团发现的质量。【方法/过程】通过构建标签的概念空间挖掘标签间的语义关系,并根据标签包含的隐性社会网络发现用户网络,进而将两者结合融入到社团发现算法中,并以豆瓣网数据对模型进行实证。【结果/结论】标签概念空间及用户网络能够提升语义社团发现算法效果。
【Purpose/significance】Based on semantic information of label system and implicit social network,to construct the fusion tags concept space and semantic association found model of user network and to improve the quality of community discovery.【Method/process】By building concept space of label to mine the semantic relationship between tags,and according to the implicit social network contained in tags to discovery user network,and then converge them into the community discovery algorithm,which is verified by Douban data.【Result/conclusion】Label concept space and user network can improve the effect of semantic community discovery algorithm.
作者
易明
秦涵
蒋武轩
YI Ming;QIN Han;JIANG Wu-xuan(School of Information Management,Central China Normal University,Wuhan 430079,China)
出处
《情报科学》
CSSCI
北大核心
2020年第2期29-38,74,共11页
Information Science
基金
国家社会科学基金一般项目“基于人类动力学的社交网络信息交流行为研究”(16BTQ076).
关键词
标签系统
概念空间
隐性社会网络
语义社团发现
tagging system
concept space
implicit social network
semantic community detection