Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (5): 65-72    DOI: 10.11925/infotech.1003-3513.2015.05.09
Current Issue | Archive | Adv Search |
The Pedigree Method to Mine Influential Clusters of Topic Information in Social Network
He Jianmin1,2, Wang Zhe1
1 School of Management, Hefei University of Technology, Hefei 230009, China;
2 Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei University of Technology, Hefei 230009, China
Download: PDF(601 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] The paper aims to find the user groups (influential clusters in social network) which have great influence on others in particular topics. The user groups can be employed as spread media to support the marketing decisions of enterprises. [Methods] With the data collected from Sina micro-blog, use the pedigree method to mine the influential clusters in social network, and analyze the information distribution and interaction among individuals to mine the influential clusters. [Resuls] The proposed method can find the user groups which have high influence in social network. Enterprises can utilize the user groups to distribute the marketing information and enhance the guiding rate of product sale. [Limitations] Only consider the factor which compose the influential ability of individuals, and do not take the unconventional behaviors of micro-blog users into account. [Conclusions] This paper provides the theoretical basis and practical method to support the social marketing decisions of enterprises.

Key wordsTopic information dissemination      Frequent spread sequence      Pedigree data mining      Influence clusters discovery     
Received: 21 October 2014      Published: 11 June 2015
:  TP393  

Cite this article:

He Jianmin, Wang Zhe. The Pedigree Method to Mine Influential Clusters of Topic Information in Social Network. New Technology of Library and Information Service, 2015, 31(5): 65-72.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.05.09     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I5/65

[1] 陈福集, 胡改丽. 网络舆情热点话题传播模式研究[J]. 情报杂志, 2014, 33(1): 97-101. (Chen Fuji, Hu Gaili. On the Spread Pattern of Network Public Opinion Hot Topics [J]. Journal of Intelligence, 2014, 33(1): 97-101.)
[2] 李青, 朱恒民, 杨东超. 微博网络中舆情话题传播演化模型 [J]. 现代图书情报技术, 2013(12): 74-80. (Li Qing, Zhu Hengmin, Yang Dongchao. The Topic Evolution Model of the Public Opinion in Micro-Blogging Network [J]. New Technology of Library and Information Service, 2013(12): 74-80.)
[3] Coscia M, Giannotti F, Pedreschi D. A Classification for Community Discovery Methods in Complex Networks [J]. Statistical Analysis and Data Mining, 2011, 4(5): 512-546.
[4] 黄发良, 张师超, 朱晓峰. 基于多目标优化的网络社区发现方法[J]. 软件学报, 2013, 24(9): 2062-2077. (Huang Faliang, Zhang Shichao, Zhu Xiaofeng. Discovering Network Community Based on Multi-Objective Optimization [J]. Journal of Software, 2013, 24(9): 2062-2077.)
[5] Danon L, Díaz-Guilera A, Arenas A. Effect of Size Hetero-gen-eity on Community Identification in Complex Networks [OL]. arXiv: physics/0601144.
[6] Clauset A, Newman M E J, Moore C. Finding Community Structure in Very Large Networks [J]. Physical Review E, 2004, 70(6): 066111.
[7] 肖宇, 许炜, 商召玺. 微博用户区域影响力识别算法及分析[J]. 计算机科学, 2012, 39(9): 38-42. (Xiao Yu, Xu Wei, Shang Zhaoxi. Analysis on Algorithms of Identifying Regional Influential Users in Micro-blogging [J]. Computer Science, 2012, 39(9): 38-42.)
[8] 赵发珍. 基于链接分析法的网络社区影响力研究—以国内30个网络社区网站为例[J]. 现代情报, 2013, 33(6): 91-95. (Zhao Fazhen. Influence of Online Communities Based on Link Analysis—A Case Study of 30 Network Community of China [J]. Journal of Modern Information, 2013, 33(6): 91-95.)
[9] 吴良, 黄威靖, 陈薇, 等. ACT-LDA: 集成话题、社区和影响力分析的概率模型[J]. 计算机科学与探索, 2013, 7(8): 718-728. (Wu Liang, Huang Weijing, Chen Wei, et al. ACT-LDA: A Probabilistic Model of Topic, Community and User Influence [J]. Journal of Frontiers of Computer Science and Technology, 2013, 7(8): 718-728.)
[10] 祝帅, 郑小林, 陈德人. 论坛中的意见领袖自动发现算法研究[J]. 系统工程理论与实践, 2011, 31(S2): 7-12. (Zhu Shuai, Zheng Xiaolin, Chen Deren. Research of Algorithm for Automatic Opinion Leader Detection in BBS [J]. Systems Engineering—Theory & Practice, 2011, 31(S2): 7-12.)
[11] Matsumura N, Ohsawa Y, Ishizuka M. Influence Diffusion Model in Text-based Communication [C]. In: Proceedings of the 11th International World Wide Web Conference, Honolulu, Hawaii, USA. 2002.
[12] Song X, Chi Y, Hino K, et al. Identifying Opinion Leaders in the Blogosphere [C]. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management. ACM, 2007: 971-974.
[13] Pal A, Counts S. Identifying Topical Authorities in Microblogs [C]. In: Proceedings of the 4th International Conference on Web Search and Web Data Mining, Hong Kong, China. ACM, 2011: 45-54.
[14] 徐森, 卢志茂, 顾国昌. 基于矩阵谱分析的文本聚类集成算法 [J]. 模式识别与人工智能, 2009, 22(5): 780-786. (Xu Sen, Lu Zhimao, Gu Guochang. Document Cluster Ensemble Algorithms Based on Matrix Spectral Analysis [J]. Pattern Recognition and Artificial Intelligence, 2009, 22(5): 780-786.)
[15] 陈福集, 杨善林. 一种基于SOM的中文Web文档层次聚类方法 [J]. 情报学报, 2002, 21(2): 173-176. (Chen Fuji, Yang Shanlin. A Clustering Method for Chinese Web Document Based on SOM [J]. Journal of the China Society for Scientific and Technical Information, 2002, 21(2): 173-176.)
[16] 龚尚福, 陈婉璐, 贾澎涛. 层次聚类社区发现算法的研究[J]. 计算机应用研究, 2013, 30(11): 3216-3220. (Gong Shangfu, Chen Wanlu, Jia Pengtao. Survey on Algorithms of Community Detection [J]. Application Research of Computers, 2013, 30(11): 3216-3220.)
[17] 肖波, 徐前方, 蔺志青, 等. 可信关联规则及其基于极大团的挖掘算法[J]. 软件学报, 2008, 19(10): 2597-2610. (Xiao Bo, Xu Qianfang, Lin Zhiqing, et al. Credible Association Rule and Its Mining Algorithm Based on Maximum Clique [J]. Journal of Software, 2008, 19(10): 2597-2610.)
[18] 熊涛, 何跃. 微博转发网络中意见领袖的识别与分析 [J]. 现代图书情报技术, 2013(6): 55-62. (Xiong Tao, He Yue. The Identification and Analysis of Micro-blogging Opinion Leaders in the Network of Retweet Relationship [J]. New Technology of Library and Information Service, 2013(6): 55-62.)
[19] 唐常杰, 刘威, 温粉莲, 等. 社会网络分析和社团信息挖掘的三项探索——挖掘虚拟社团的结构、核心和通信行为[J]. 计算机应用, 2006, 26(9): 2020-2023. (Tang Changjie, Liu Wei, Wen Fenlian, et al. Three Probes into the Social Network Analysis and Consortium Information Mining—Mining the Structure, Core and Communication Behavior of Virtual Consortium [J]. Journal of Computer Applications, 2006, 26(9): 2020-2023.)
[20] 夏明波, 王晓川, 孙永强, 等. 序列模式挖掘算法研究[J]. 计算机技术与发展, 2006, 16(4): 4-6. (Xia Mingbo, Wang Xiaochuan, Sun Yongqiang, et al. Research on Sequential Pattern Mining Algorithms [J]. Computer Technology and Development, 2006, 16(4): 4-6.)
[21] 刘丽娜, 沈继红, 朱强华, 等. 基于频繁传播模式的影响群落发现方法[J]. 吉林大学学报: 工学版, 2013, 43(2): 444-450. (Liu Li'na, Shen Jihong, Zhu Qianghua, et al. Discovering Tribe-leaders Based on Frequent Pattern of Propagation [J]. Journal of Jilin University: Engineering and Technology Edition, 2013, 43(2): 444-450.)
[22] Pei J, Han J, Mortazavi-Asl B, et al. Prefixspan: Mining Sequential Patterns Efficiently by Prefix-projected Pattern Growth [C]. In: Proceedings of the 17th International Conference on Data Engineering. IEEE, 2001: 215-224.
[23] Han J, Pei J, Mortazavi-Asl B, et al. FreeSpan: Frequent Pattern-projected Sequential Pattern Mining [C]. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2000: 355-359.
[24] Ester M, Kriegel H P, Sander J, et al. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise [C]. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, USA. AAAI Press, 1996: 226-231.

[1] Qingtian Zeng,Xiaohui Hu,Chao Li. Extracting Keywords with Topic Embedding and Network Structure Analysis[J]. 数据分析与知识发现, 2019, 3(7): 52-60.
[2] Lixin Xia,Jieyan Zeng,Chongwu Bi,Guanghui Ye. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
[3] Yang Ning, Huang Feihu, Wen Yi, Chen Yunwei. An Opinion Evolution Model Based on the Behavior of Micro-blog Users[J]. 现代图书情报技术, 2015, 31(12): 34-41.
[4] Yu Xincong, Li Honglian, Lv Xueqiang. Research on the Application of Hyponymy in the Enrollment Robot[J]. 现代图书情报技术, 2015, 31(12): 65-71.
[5] Wang Zhengjun, Yu Xiaoyi, Jin Yuling. Using Sniffer Technology to Constraint Electronic Resource Excessive Downloading[J]. 现代图书情报技术, 2015, 31(12): 95-100.
[6] Liu Zhanbing, Xiao Shibin. Collaborative Filtering Recommended Algorithm Based on User's Interest Fuzzy Clustering[J]. 现代图书情报技术, 2015, 31(11): 12-17.
[7] Wu Wankun, Wu Qinglie, Gu Jinjiang. Hot Topic Extraction from E-commerce Microblog Based on EM-LDA Integrated Model[J]. 现代图书情报技术, 2015, 31(11): 33-40.
[8] Qiang Shaohua, Wu Peng. The Research of Spatial Measure of Users' Mental Model of Website Category from the View of Regional Differences[J]. 现代图书情报技术, 2015, 31(11): 68-74.
[9] Qin Xuedong. Solution for KVM Private Cloud Management System Based on Drupal[J]. 现代图书情报技术, 2015, 31(11): 91-95.
[10] Wu Jiang, Zhang Jinfan. Research on Follow Influence of Triadic Structure in Social Network——Take Student Relation Network as an Example[J]. 现代图书情报技术, 2015, 31(10): 72-80.
[11] Jiang Chuntao. Automatic Annotation of Bibliographical References in Chinese Patent Documents[J]. 现代图书情报技术, 2015, 31(10): 81-87.
[12] Wang Ying, Zhang Zhixiong, Li Chuanxi, Liu Yi, Tang Yijie, Zhou Zijian, Qian Li, Fu Honghu. The Design and Implementation of Open Engine System for Scientific & Technological Knowledge Organization Systems[J]. 现代图书情报技术, 2015, 31(10): 95-101.
[13] Gui Sisi, Lu Wei, Huang Shihao, Zhou Pengcheng. User Interest Prediction Combing Topic Model and Multi-time Function[J]. 现代图书情报技术, 2015, 31(9): 9-16.
[14] Qin Xiaohui, Le Xiaoqiu. Topic Sources and Trends Tracking Towards Citation Network of Single Paper[J]. 现代图书情报技术, 2015, 31(9): 52-59.
[15] Deng Qiping, Wang Xiaomei. Identifying Influential Authors Based on LeaderRank[J]. 现代图书情报技术, 2015, 31(9): 60-67.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn