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

Rao Dong-ning; Wen Yuan-li; Wei lai; Wang Ya-li

doi:10.12052/gdutxb.170023

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

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

更新时间：2026-05-27

- A Weighted Centrality Algorithm for Social Networks Based on Spark Platform in Different Cultural Environments
- Vol. 34, Issue 3, Pages: 15-20,48(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170023
  CLC： TP182
- Online First：09 May 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
饶东宁, 温远丽, 魏来, 王雅丽. 基于Spark平台的社交网络在不同文化环境中的中心度加权算法[J]. 广东工业大学学报, 2017, 34(3): 15-20,48.

Rao Dong-ning, Wen Yuan-li, Wei lai, et al. A Weighted Centrality Algorithm for Social Networks Based on Spark Platform in Different Cultural Environments[J]. 2017, 34(3): 15-20,48.
饶东宁, 温远丽, 魏来, 王雅丽. 基于Spark平台的社交网络在不同文化环境中的中心度加权算法[J]. 广东工业大学学报, 2017, 34(3): 15-20,48. DOI： 10.12052/gdutxb.170023.

Rao Dong-ning, Wen Yuan-li, Wei lai, et al. A Weighted Centrality Algorithm for Social Networks Based on Spark Platform in Different Cultural Environments[J]. 2017, 34(3): 15-20,48. DOI： 10.12052/gdutxb.170023.

摘要

社交网络广泛应用于科技、商业、经济和生物等领域.社交网络一般用中心性指标来对节点的重要性进行量化，常用的中心性指标有节点中心度、接近中心度、介数中心度、三角计数等等.已有的中心度算法通常只考虑单一的度量标准，本文提出加权中心度的思想，结合不同的中心度指标来进行综合考虑.该实验使用社交网络的真实BoardEX数据库，由合作单位香港大学提供，基础数据约600 G，需借助Apache Spark处理大数据的能力来进行集群并行计算.社交网络数据分成美国、英国、欧洲和其他国家4个地区，计算各地区上市公司的首席技术官和首席信息官的个人中心度，从而得到每个地区的平均加权中心度.实验结果表明，通过调整权值，可以使不同区域的加权中心度的差异尽可能小，且由权值大小可知不同中心度度量标准对加权中心度的影响不同.基于真实数据库和处理大数据的集群计算，本文的研究成果更具有现实意义和应用前景.

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.

关键词

Keywords

references

LARCKER D F, SO E C, WANG C C. Boardroom centrality and stock returns[J]. Journal of Accounting & Economics, 2013, 55:225-250.

HWANG B H, KIM S. It pays to have friends[J]. Journal of Financial Economics, 2009, 93:138-158.

FRACASSI C, TATE G. External networking and internal firm governance[J]. Journal of Finance, 2012, 67(1):153-194.

ISHII J, XUAN Y. Acquirer-target social ties and merger outcomes[J]. Journal of Financial Economics, 2014, 112:344-363.

FALEYE O, HOITASH R, HOITASH U. The costs of intense board monitoring[J]. Journal of Financial Economics, 2011, 101(1):160-181.

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.

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.

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.

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.

JACKSON M O. Social and economic networks[M]. Princeton:Princeton University Press, 2010.

CAI Y, SEVILIR M. Board connections and M&A transactions[J] Journal of Financial Economics, 2012, 103:327-349.

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.

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.

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.

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.

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.

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.

PAGEL, BRINS, MOTWASNIR, et al. The PageRank citation ranking:bringing order to the web[R].[S.l.]:Stanford InfoLab, 1999.

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.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Construction and Application of the Production Operation & Decision-making System Based on Big Data for Transport Group

Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks

Research on Identifying Brand Competition Relationships with Consumer Browsing Behavior

Research on using information flow and HowNet to build big data semantic sharing channel

Research on Applied Models Based on Storm

Related Author

Li Zhi-meng

Li Wei-hua

Wang Ya-li

Wei lai

Wang Jun-xing

Rao Dong-ning

He Rui-chao

Fan Meng-ting

Related Institution

School of Information Science and Technology, Huizhou University

Guangdong Yunwei for Big Data Technology Consulting Limited Company

School of Computers, Guangdong University of Technology

School of Economics and Finance, The University of Hong Kong

School of Economics and Management, South China Normal University

AI问答

⁰