Journal of Guangdong University of Technology ›› 2017, Vol. 34 ›› Issue (03): 15-20.doi: 10.12052/gdutxb.170023

Previous Articles     Next Articles

A Weighted Centrality Algorithm for Social Networks Based on Spark Platform in Different Cultural Environments

Rao Dong-ning1, Wen Yuan-li1, Wei lai2, Wang Ya-li3   

  1. 1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Economics and Finance, the University of Hong Kong, Hong Kong 999077, China;
    3. School of Economics and Management, South China Normal University, Guangzhou 510631, China
  • Received:2017-01-15 Online:2017-05-09 Published:2017-05-09

HTML

PDF (PC)

3379

Abstract:

Social networks are developed rapidly and used widely in the fields such as science and technology, business, economic and biological fields. People often use the centrality to quantify the importance degree of nodes in a social network. However, in the existing centrality algorithms, researchers only use a single centrality measuring, without considering the co-effects of different measuring. Therefore, a weighted centrality is proposed which is a function of different centrality measuring. Experiments here use a real social network database BoardEX, which is provided by our cooperative research institution, the University of Hong Kong. The size of the database is about 600G. This inspires us to use the Apache Spark platform to calculate such a big data. The experimental social network is divided into four regions:the U.S.A, the United Kingdom, Europe, others. First, the degree centrality of some persons, e.g. the chief technology officers or the chief information officers in a quoted company, in each region, is calculated. Then, a weighted function is constructed to calculate the average centrality. Experimental results show that, by setting the weighted values, the difference between the weighted centrality of regions is minimized. Besides, the weighted values reflect the contributions of various centrality measuring to the weighted centrality. With the application of real social network database and big data cluster computing, a more practical and promising application prospect is showed.

Key words: social networks, big data, centrality, weighted centrality

CLC Number: 

  • TP182

[1] LARCKER D F, SO E C, WANG C C. Boardroom centrality and stock returns[J]. Journal of Accounting & Economics, 2013, 55:225-250.
[2] HWANG B H, KIM S. It pays to have friends[J]. Journal of Financial Economics, 2009, 93:138-158.
[3] FRACASSI C, TATE G. External networking and internal firm governance[J]. Journal of Finance, 2012, 67(1):153-194.
[4] ISHII J, XUAN Y. Acquirer-target social ties and merger outcomes[J]. Journal of Financial Economics, 2014, 112:344-363.
[5] FALEYE O, HOITASH R, HOITASH U. The costs of intense board monitoring[J]. Journal of Financial Economics, 2011, 101(1):160-181.
[6] SURI N, NARAHAR Y. A shapley value based approach to discover influential nodes in social networks[J]. IEEE Trans Autom Sci Eng, 2010, 99:1-18.
[7] 郑巍, 潘倩, 邓宇凡. 移动社交网络中基共同邻居网络中心度的链路预测方法[J]. 计算机应用研究, 2016, 33(9):2743-2746. ZHENG W, PAN Q, DENG Y F. Link prediction algorithm based on common neighbors network centrality in mobile social networks[J]. Application Research of Computers, 2016, 33(9):2743-2746.
[8] 李明雪. 基于社会网络的社区发现和中心性分析算法研究[D]. 长春:吉林大学计算机科学与技术学院, 2016.
[9] 郭静. 社会网络影响力传播的分析与挖掘研究[D]. 北京:北京邮电大学计算机学院, 2014.
[10] JACKSON M O. Social and economic networks[M]. Princeton:Princeton University Press, 2010.
[11] CAI Y, SEVILIR M. Board connections and M&A transactions[J] Journal of Financial Economics, 2012, 103:327-349.
[12] 李静茹, 喻莉, 赵佳. 加权社交网络节点中心性计算模型[J]. 电子科技大学学报, 2014, 43(3):322-328. LI J R, YU L, ZHAO J. A Node Centrality evaluation model for weighted social networks[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(3):322-328.
[13] 刘欣, 李鹏, 刘璟, 等. 社交网络节点中心性测度[J]. 计算机工程与应用, 2014, 50(5):116-120. LIU X, LI P, LIU J, et al. Centrality for nodes in social networks[J]. Computer Engineering and Applications, 2014, 50(5):116-120.
[14] 王晓彤. 基于PageRank的微博用户影响力度量[J]. 广东工业大学学报, 2016, 33(3):49-54. WANG X T. An evaluation of microblog users'influence based on Page Rank[J]. Journal of Guangdong University of Technology, 2016, 33(3):49-54.
[15] 林穗, 赵菲. 基于Spark的线性模型在广告投放系统中的应用研究[J]. 广东工业大学学报, 2016, 33(5):28-33. LIN S, ZHAO F. An application research of linear model in the advertising system based on Spark[J]. Journal of Guangdong University of Technology, 2016, 33(5):28-33.
[16] 曾碧, 林展鹏, 邓杰航. 自主移动机器人走廊识别算法研究与改进[J]. 广东工业大学学报, 2016, 33(5):9-21. ZENG B, LIN Z P, DENG J H. Algorithm research on recognition and improvement for corridor of autonomous mobile robot[J]. Journal of Guangdong University of Technology, 2016, 33(5):9-21.
[17] 陆靖桥, 傅秀芬, 蒙在桥. 复杂网络的鲁棒性与中心性指标的研究[J]. 广东工业大学学报, 2016, 33(4):302-308. LU J Q, FU X F, MENG Z Q. Research on robustness and centrality metrics of complex networks[J]. Journal of Guangdong University of Technology, 2016, 33(4):302-308.
[18] PAGEL, BRINS, MOTWASNIR, et al. The PageRank citation ranking:bringing order to the web[R].[S.l.]:Stanford InfoLab, 1999.
[19] 金宏桥, 董一鸿. 大数据下图三角计算的研究进展[J]. 电信科学, 2016, (6):153-162. JIN H Q, DONG Y H. Research progress of triangle counting in big data[J]. Telecommunications Science, 2016, (6):153-162.
[1] Rao Dong-ning, Lin Zhuo-yi, Wei Lai. n-Degree and k-Stress Centrality with Parallel Algorithms [J]. Journal of Guangdong University of Technology, 2020, 37(03): 36-41.
[2] Liu Hong-wei, Liang Zhou-yang, Zuo Mei-hua, Lu Dan, Fan Meng-ting, He Rui-chao. Research on Identifying Brand Competition Relationships with Consumer Browsing Behavior [J]. Journal of Guangdong University of Technology, 2019, 36(05): 1-6,13.
[3] Huang Jian-wen, Zhu Xue-mei, Xu Ying, Xiong Lu, Zhang Wei-guo. A Quantitative Research on Spatial Morphology of Old Residential Neighborhoods in Lingnan Area Based on Multi-source Big Data [J]. Journal of Guangdong University of Technology, 2019, 36(04): 70-79.
[4] Bai Jie, Yao Jia-jing, Zhang Mao-jun, Li Qiao-xing. A Simple Search Algorithm on Conditionally Uncorrelated Volatility Models in Financial Big Data [J]. Journal of Guangdong University of Technology, 2018, 35(05): 26-30.
[5] Sun Wei-jun, Xie Sheng-li, Wang Gu-yin, Diao Jun-wu, Ruan Hang. Design and Implementation of Industrial Big Data Cloud Platform for Smart Factory [J]. Journal of Guangdong University of Technology, 2018, 35(03): 67-71.
[6] Li Wei-hua, Li Zhi-meng. Construction and Application of the Production Operation & Decision-making System Based on Big Data for Transport Group [J]. Journal of Guangdong University of Technology, 2018, 35(03): 113-118.
[7] Rao Dong-ning, Wang Jun-xing, Wei lai, Wang Ya-li. Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks [J]. Journal of Guangdong University of Technology, 2018, 35(02): 46-50.
[8] Xie Zhen-dong, Wu Jin-cheng, Li Zhi-ming, Wu Guan-hua. A Study of Construction and Cultivation of Big Data Capacity of Enterprise [J]. Journal of Guangdong University of Technology, 2017, 34(03): 110-114.
[9] Liu Dong-ning, Lu Ming-jun, Huang Bao-ying, Liang Lu. Group Role Assignment and its Optimization with Preorder Constraints [J]. Journal of Guangdong University of Technology, 2017, 34(03): 21-29.
[10] Mao Li-na, Li Wei-hua. Research on using information flow and HowNet to build big data semantic sharing channel [J]. Journal of Guangdong University of Technology, 2017, 34(03): 30-35.
[11] WANG Xiao-Tong. An Evaluation of Microblog Users’ Influence Based on PageRank [J]. Journal of Guangdong University of Technology, 2016, 33(03): 49-54.
[12] TAN Si-Ni, CHEN Ping-Hua. Identifying the Influential User in the Cobweb State Micro-blog Network [J]. Journal of Guangdong University of Technology, 2015, 32(3): 61-66.
[13] LI Qiao-Xing, QIANG Bao-Hua, YANG Chun-Yan. The Storage Model of Big Data Basicelements in HBase Database and Its Realization [J]. Journal of Guangdong University of Technology, 2014, 31(3): 8-13.
[14] WANG Yong, XU Zhong-Tao, WANG Ying. Research and Implementation on Competitive Intelligence System Based on Big Data [J]. Journal of Guangdong University of Technology, 2014, 31(3): 27-31.
[15] LIU Hong-Wei, LIU Zhi-Hui, ZHU Hui, LU Tao. Privacy-preserving Algorithm for Cross-organizational Collaborative [J]. Journal of Guangdong University of Technology, 2014, 31(3): 21-26.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © 2017 Journal of Guangdong University of Technology
Add：729 East Dongfeng RD., Guangzhou PRC. Postcode: 510090
Tel：020-37626653,020-37626139,020-37626396
Email：xbzrb@gdut.edu.cn
Supported:Beijing Magtech