Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (3): 8-17    DOI: 10.11925/infotech.1003-3513.2016.03.02
Orginal Article Current Issue | Archive | Adv Search |
Hypernetwork Model for Semantic Social Network and Automatic Identification of Key Nodes
Zhang Lei,Ma Jing(),Li Dandan,Shen Yang
College of Economic and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study aims to identify the key nodes of public opinion spread and evolution based on the semantic social network model. [Methods] We first built model for Weibo semantic social network with the help of hypernetwork theory, and then used emotion Ontology and LDA model to quantify nodes. Finally, we established the hyper edge sorting algorithm to identify the key nodes. [Results] The proposed model could effectively and acturately quantify those nodes from real Weibo data. [Limitations] We did not explore the results of the proposed method’s real-time performance, and new ways of leading the public opinion after identifying those key nodes. [Conclusions] This study provides a solution for the government to identify the key nodes in the social network systems, and then reduce the impacts of negative contents to the healthy development of the Internet.
Key words： Hypernetwork      Semantic social network      Key node identification      LDA model      Emotion Ontology     
Received: 08 October 2015      Published: 12 April 2016
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Zhang Lei
Ma Jing
Li Dandan
Shen Yang

Cite this article:

Zhang Lei,Ma Jing,Li Dandan,Shen Yang. Hypernetwork Model for Semantic Social Network and Automatic Identification of Key Nodes. New Technology of Library and Information Service, 2016, 32(3): 8-17.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2016.03.02     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2016/V32/I3/8

[1] 辛宇, 杨静, 谢志强. 基于随机游走的语义重叠社区发现算法[J]. 计算机研究与发展, 2015, 52(2): 499-511.
[1] (Xin Yu, Yang Jing, Xie Zhiqiang.A Semantic Overlapping Community Detecting Algorithm in Social Network Based on Random Walk[J]. Journal of Computer Research and Development, 2015, 52(2): 499-511.)
[2] 任晓龙, 吕琳媛. 网络重要节点排序方法综述[J]. 科学通报, 2014, 59(13): 1175-1197.
[2] (Ren Xiaolong, Lv Linyuan.Review of Ranking Nodes in Complex Networks[J]. Chinese Science Bulletin, 2014, 59(13): 1175-1197.)
[3] Weng J, Lim E P, Jiang J, et al.Twitter Rank: Finding Topic-sensitive Influential Twitterers[C]. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2010: 261-270.
[4] 康伟. 基于SNA的突发事件网络舆情关键节点识别——以“7·23动车事故”为例[J]. 公共管理学报, 2012, 9(3): 101-128.
[4] (Kang Wei.Analysis of the Key Nodes in Public Opinion Spread During Emergencies Based on Social Network Theory——A Case Study of the 7·23 Wenzhou High-speed Train Collision[J]. Journal of Public Management, 2012, 9(3): 101-128.)
[5] 曹学艳, 段飞飞, 方宽, 等. 网络论坛视角下突发事件舆情的关键节点识别及分类研究[J]. 图书情报工作, 2014, 58(4): 65-70.
[5] (Cao Xueyan, Duan Feifei, Fang Kuan, et al.Research of Identification and Classification of Emergencies Key Nodes Based on BBS[J]. Library and Information Service, 2014, 58(4): 65-70.)
[6] 武澎, 王恒山, 李煜. 突发事件信息传播超网络中枢纽节点的判定研究[J]. 管理评论, 2013, 25(6): 104-111.
[6] (Wu Peng, Wang Hengshan, Li Yu.Determination of the Hub Nodes in the Emergencies’ Information Dissemination Supernetwork[J]. Management Review, 2013, 25(6): 104-111.)
[7] Bonacich P.Factoring and Weighting Approaches to Status Scores and Clique Identification[J]. The Journal of Mathematical Sociology, 1972, 2(1): 113-120.
[8] Zhang Z K, Zhou T, Zhang Y C.Tag-aware Recommender Systems: A State-of-the-art Survey[J]. Journal of Computer Science and Technology, 2011, 26(5): 767-777.
[9] 赫南, 李德毅, 淦文燕, 等. 复杂网络中重要性节点发掘综述[J]. 计算机科学, 2007, 34(12): 1-5, 17.
[9] (He Nan, Li Deyi, Gan Wenyan, et al.Mining Vital Nodes in Complex Networks[J]. Computer Science, 2007, 34(12): 1-5, 17.)
[10] Dolev S, Elovici Y, Puzis R. Routing Betweenness Centrality [J]. Journal of the ACM, 2010, 57(4): Article No.25.
[11] Kitsak M, Gallos L K, Havlin S, et al.Identifying Influential Spreaders in Complex networks[J]. Nature Physics, 2010, 6(11): 888-893.
[12] 许进. 一种研究系统的新方法——核与核度法[J]. 系统工程与电子技术, 1994(6): 1-10.
[12] (Xu Jin.A New Method of Studying System——System Core and Coritivity[J]. Systems Engineering and Electronics, 1994(6): 1-10.)
[13] Klemm K, Serrano M A, Eguiluz V M, et al. A Measure of Individual Role in Collective Dynamics [J]. Scientific Reports, 2012, 2(2). Article No. 292.
[14] Aral S, Walker D.Identifying Influential and Susceptible Members of Social Networks[J]. Science, 2012, 337(6092): 337-341.
[15] 马宁, 刘怡君. 基于超网络中超边排序算法的网络舆论领袖识别[J]. 系统工程, 2012, 31(9): 1-10.
[15] (Ma Ning, Liu Yijun.Identification of Public Opinion Leader Based on the SuperEdgeRank Algorithm in Hypernetwork[J]. Systems Engineering, 2012, 31(9): 1-10.)
[16] Nagurney A, Dong J.Supernetworks: Decision-making for the Information Age [M]. Edward, Elgar Publishing, Incorporated, 2002.
[17] Nagurney A.Supernetworks: An Introduction to the Concept and Its Applications with a Specific Focus on Knowledge Supernetworks[J]. International Journal of Knowledge Culture and Change Management, 2005(4): 1-16.
[18] 王志平, 王众托. 超网络理论及其应用[M]. 北京: 科学出版社, 2008.
[18] (Wang Zhiping, Wang Zhongtuo.Hypernetwork Theory and Application [M]. Beijing: Science Press, 2008.)
[19] Lin J, Dai F, Li B C, et al.Electromagnetic Compatibility Supernetwork Modeling and Node Importance Evaluation [C]. In: Proceedings of the 5th International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE Conference Publications, 2013: 306-310.
[20] Deng Z.Application of Crowd Importance Modeling in Regional Development Based on Super Network[J]. Value Engineering, 2015, 13: 211-212.
[21] 武澎, 王恒山. 基于特征向量中心性的社交信息超网络中重要节点的评判[J]. 情报理论与实践, 2014, 37(5): 107-113.
[21] (Wu Peng, Wang Hengshan.Key Nodes in Social Information Hypernetwork Evaluation Based on Eigenvector Centrality[J]. Information Studies: Theory & Application, 2014, 37(5): 107-113.)
[22] 陈建美. 中文情感词汇本体的构建及其应用[D]. 大连: 大连理工大学, 2008.
[22] (Chen Jianmei.The Construction and Application of Chinese Emotion Word Ontology [D]. Dalian: Dalian University of Technology, 2008.)
[23] Blei D, Ng A, Jordan M, et al.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[24] 袁立庠. 微博的传播模式与传播效果[J]. 安徽师范大学学报: 人文社会科学版, 2011, 39(6): 678-683.
[24] (Yuan Liyang.Communication Modes and Effects of Twitter[J]. Journal of Anhui Normal University: Humanities and Social Sciences, 2011, 39(6): 678-683.)
[25] Gourab G, Vinko Z, Guido C, et al.Random Hypergraphs and Their Applications[J]. Physical Review E, 2009, 79(6): 853-857.
[26] Wang J W, Rong L L, Deng Q H, et al.Evolving Hypernetwork Model[J]. The European Physical Journal B, 2010, 77(4): 493-498.
[27] Kullback S, Leibler R A.On Information and Sufficiency[J]. The Annals of Mathematical Statistics, 1951, 22(1): 79-86.
[1] Cai Yongming,Liu Lu,Wang Kewei. Identifying Key Users and Topics from Online Learning Community[J]. 数据分析与知识发现, 2020, 4(6): 69-79.
[2] Liu Yuwen,Wang Kai. Finding Geographic Locations of Popular Online Topics[J]. 数据分析与知识发现, 2020, 4(2/3): 173-181.
[3] Ye Guanghui,Xu Tong,Bi Chongwu,Li Xinyue. Analyzing Evolution of City Tourism Portraits with Multi-Dimensional Features and LDA Model[J]. 数据分析与知识发现, 2020, 4(11): 121-130.
[4] Yunfei Shao,Dongsu Liu. Classifying Short-texts with Class Feature Extension[J]. 数据分析与知识发现, 2019, 3(9): 60-67.
[5] Xu Yanhua,Miao Yujie,Miao Lin,Lv Xueqiang. Generating HSK Writing Essays with LDA Model[J]. 数据分析与知识发现, 2018, 2(9): 80-87.
[6] Wang Li,Zou Lixue,Liu Xiwen. Visualizing Document Correlation Based on LDA Model[J]. 数据分析与知识发现, 2018, 2(3): 98-106.
[7] Wang Jingqi,Li Rui,Wu Huayi. The Evolution of Online Public Opinion Based on Spatial Autocorrelation[J]. 数据分析与知识发现, 2018, 2(2): 64-73.
[8] Li Zhen,Ding Shengchun,Wang Nan. Identifying Topics of Online Public Opinion[J]. 数据分析与知识发现, 2017, 1(8): 18-30.
[9] Fang Xiaofei,Huang Xiaoxi,Wang Rongbo,Chen Zhiqun,Wang Xiaohua. Identifying Hot Topics from Mobile Complaint Texts[J]. 数据分析与知识发现, 2017, 1(2): 19-27.
[10] Ruyi Yang,Dongsu Liu,Hui Li. An Improved Topic Model Integrating Extra-Features[J]. 现代图书情报技术, 2016, 32(1): 48-54.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn