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
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600G,需借助ApacheSpark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...
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| Published in | 广东工业大学学报 Vol. 34; no. 3; pp. 15 - 20 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
广东工业大学 计算机学院,广东 广州,510006%香港大学 经济与金融学院,中国 香港,999077%华南师范大学 经济与管理学院,广东 广州,510631
2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-7162 |
| DOI | 10.12052/gdutxb.170023 |
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| Abstract | 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600G,需借助ApacheSpark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. |
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| AbstractList | TP182; 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. 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600G,需借助ApacheSpark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_FL | 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. |
| Author | 饶东宁;温远丽;魏来;王雅丽 |
| AuthorAffiliation | 广东工业大学计算机学院,广东广州510006;香港大学经济与金融学院,中国香港999077;华南师范大学经济与管理学院,广东广州510631 |
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| Author_FL | Rao Dong-ning Wei lai Wang Ya-li Wen Yuan-li |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | 中心性 社交网络 加权中心度 centrality social networks weighted centrality big data 大数据 |
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| Notes | social networks; big data; centrality; weighted centrality 44-1428/T 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 about600G.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 |
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| Publisher | 广东工业大学 计算机学院,广东 广州,510006%香港大学 经济与金融学院,中国 香港,999077%华南师范大学 经济与管理学院,广东 广州,510631 |
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| Snippet | 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.... TP182; 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... |
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| SubjectTerms | 社交网络;大数据;中心性;加权中心度 |
| Title | 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 |
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