[1]张燕平,鲍媛媛,赵姝,等.基于学术会议的科研合作网络微观结构动态演化分析[J].智能系统学报编辑部,2015,10(04):620-626.[doi:10.3969/j.issn.1673-4785.201505052]
 ZHANG Yanping,BAO Yuanyuan,ZHAO Shu,et al.Dynamic microscopic structure evolution analysis for the author collaboration network of academic conferences[J].CAAI Transactions on Intelligent Systems,2015,10(04):620-626.[doi:10.3969/j.issn.1673-4785.201505052]
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基于学术会议的科研合作网络微观结构动态演化分析(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年04期
页码:
620-626
栏目:
出版日期:
2015-08-25

文章信息/Info

Title:
Dynamic microscopic structure evolution analysis for the author collaboration network of academic conferences
作者:
张燕平12 鲍媛媛12 赵姝12 陈洁12 黄梦晗12
1. 安徽大学 计算机科学与技术学院, 安徽 合肥 230601;
2. 安徽大学 计算智能与信号处理教育部重点实验室, 安徽 合肥 230601
Author(s):
ZHANG Yanping12 BAO Yuanyuan12 ZHAO Shu12 CHEN Jie12 HUANG Menghan12
1. School of Computer Science and Technology, Anhui University, Hefei 230601, China;
2. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China
关键词:
演化科研合作网意见领袖结构洞微观结构PageRank学术会议引用数
Keywords:
evolutionauthor collaboration networkopinion leaderstructural holemicroscopic structurePageRankacademic conferencesnumber of citations
分类号:
TP399
DOI:
10.3969/j.issn.1673-4785.201505052
文献标志码:
A
摘要:
学术科研合作网络的演化特征研究,对掌握特定学术会议的发展有着重要的意义。目前主要针对合作网络的宏观特征演化进行分析,然而个体研究者的演化规律同样有着重要的作用。使用DBLP数据库系统提供的人工智能与模式识别领域的学术会议论文作者信息以及清华大学ArnetMiner系统下载的公用数据集作为原始数据,对合作发表论文形成的合作网络进行了研究。基于意见领袖和结构洞,从微观方面分析科研合作网络随时间发展的演化规律。实验结果表明:在给出的数据集上,以引用数作为意见领袖影响力衡量指标,按照PageRank值和度中心性指标比特征向量、接近中心性、介数中心性指标具有更好的刻画节点影响力的能力;在该网络演化过程中,结构洞变化相比于意见领袖的变化较稳定,每年占据结构洞的作者基本相同。结构洞的占据者大部分是意见领袖,并且这些作者在网络中占据结构洞的程度越来越大;且在网络演化过程中,结构洞占据者有可能成为意见领袖。
Abstract:
This paper makes an analysis on the author collaboration network of academic conferences. Based on the opinion leaders and structural holes, the evolution law of the collaboration network of academic conferences developing with time is analyzed from the microscopic view. The evolution characteristics of the academic author collaboration network have very important significance for grasping the development of the particular academic conferences. Currently, researchers mainly focus on the evolution analysis of macroscopic characteristics, but the evolution law of the individual researchers also plays an important role. Author information of conference papers in the field of artificial intelligence and pattern recognition provided by the Database systems and logic programming (DBLP) and the public datasets downloaded from the ArnetMiner system of Tsinghua University are taken as the original data. The experimental results show that in the given dataset, when using the number of citations as the index to measure the opinion leader, the indexes of PageRank and degree centrality are more capable of describing node’s influence than eigenvector, closeness centrality, betweenness centrality, and in the network evolution process. The change of structural hole is more stable than that of opinion leader, and the authors occupying structural holes are basically consistent each year. Most of structural hole occupiers are opinion leaders, also the ability of occupying structural holes is becoming better and better. In the process of network evolution, the structural hole occupiers are likely to be opinion leaders.

参考文献/References:

[1] De SOLLA P D S. Little science, big science [M]. New York: Columbia University Press, 1963.
[2] NEWMAN M E J. The structure of scientific collaboration networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2001, 98(2): 404-409.
[3] BARABÁSI AL, JEONG H, NÉDA Z, et al. Evolution of the social network of scientific collaborations[J]. Physica A: Statistical Mechanics and Its Applications, 2002, 311(3/4): 590-614.
[4] TOMASSINI M, LUTHI L. Empirical analysis of the evolution of a scientific collaboration network[J]. Physica A: Statistical Mechanics and Its Applications, 2007, 385(2): 750-764.
[5] PERC M. Growth and structure of Slovenia’s scientific collaboration network[J]. Journal of Informetrics, 2010, 4(3): 475-482.
[6] RONDA-PUPO G A, GUERRAS-MARTÍN L A. Dynamics of the scientific community network within the strategic management field through the Strategic Management Journal 1980-2009: The role of cooperation[J]. Scientometrics, 2010, 85(3): 821-848.
[7] KIM H, YOON J W, CROWCROFT J. Network analysis of temporal trends in scholarly research productivity[J]. Journal of Informetrics, 2012, 6(1): 97-110.
[8] 胡枫, 赵海兴, 何佳倍, 等. 基于超图结构的科研合作网络演化模型[J]. 物理学报, 2013, 62(19): 198901. HU Feng, ZHAO Haixing, HE Jiabei, et al. An evolving model for hypergraph-structure-based scientific collaboration networks[J]. Acta Physica Sinica, 2013, 62(19): 198901.
[9] 李季, 汪秉宏, 蒋品群, 等. 节点数加速增长的复杂网络生长模型[J]. 物理学报, 2006, 55(8): 4051-4057. LI Ji, WANG Binghong, JIANG Pinqun, et al. Growing complex network model with acceleratingly increasing number of nodes[J]. Acta Physica Sinica, 2006, 55(8): 4051-4057.
[10] 苑卫国, 刘云, 程军军. 微博网络中用户特征量和增长率分布的研究[J]. 计算机学报, 2014, 37(4): 767-778. YUAN Weiguo, LIU Yun, CHENG Junjun. Research on the user characteristics and growth rates distribution in microblog[J]. Chinese Journal of Computers, 2014, 37(4): 767-778.
[11] CHI Liping. Measuring microscopic evolution processes of complex networks based on empirical data[J]. Journal of Physics Conference Series, 2015, 604(1): 1-7.
[12] MADAAN G, JOLAD S. Evolution of scientific collaboration networks[C]// 2014 IEEE International Conference on Big Data (Big Data). Washington, DC: IEEE, 2014: 7-13.
[13] ALVES B L, BENEVENUTO F, LAENDER A H F. The role of research leaders on the evolution of scientific communities[C] //Proceedings of the 22nd International Conference on World Wide Web Companion. Rio de Janeiro, Brazil, 2013: 649-656.
[14] GREENE D, DOYLE D, CUNNINGHAM P. Tracking the evolution of communities in dynamic social networks[C]// 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Odense: IEEE, 2010: 176-183.
[15] VAN NGUYEN M, KIRLEY M, GARCIA-FLORES R. Community evolution in a scientific collaboration network[C]// 2012 IEEE Congress on Evolutionary Computation (CEC). Brisbane: IEEE, 2012: 1-8.
[16] 任晓龙, 吕琳媛. 网络重要节点排序方法综述[J]. 中国科学, 2014, 59(13): 1175-1197. REN Xiaolong, LYU Linyuan. Review of ranking nodes in complex networks[J]. Chinese Science Bulletin, 2014, 59(13): 1175-1197.
[17] 王文钊, 王斌强. 基于网络中心性分析的虚拟网络映射算法[J]. 计算机应用研究, 2015, 32(2): 565-568. WANG Wenzhao, WANG Binqiang. Virtual network embedding algorithm based on analysis of network centrality[J]. Application Research of Computers, 2015, 32(2): 565-568.
[18] RANI P, SINGH E S. An offline SEO (search engine optimization) based algorithm to calculate web page rank according to different parameters[J]. International Journal of Computers & Technology, 2013, 9(1): 926-931.
[19] LOU Tiancheng, TANG Jie. Mining structural hole spanners through information diffusion in social networks[C]//Proceedings of the 22nd International Conference on World Wide Web. Rio de Janeiro, Brazil, 2013: 825-836.
[20] LEY M. DBLP system[EB/OL]. (2015-03-01)http://dblp.uni-trier.de/xml/.
[21] TANG J. Social influence analysis in large-scale social network[EB/OL]. (2015-03-01).http://arnetminer.org/lab-datasets/soinf/.

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备注/Memo

备注/Memo:
收稿日期:2015-05-29;改回日期:。
基金项目:国家自然科学基金资助项目(61175046;61402006);安徽省自然科学基金资助项目(1508085MF113);安徽省高等学校自然科学研究资助项目(KJ2013A016);安徽大学国家级大学生创新创业训练计划资助项目(201410357041).
作者简介:张燕平,女,1962年生,教授,主要研究方向为智能计算与商空间理论、机器学习、三支决策等。主持完成省基金项目、省产学研项目多项,参加了多项国家973及国家重点基金项目;主持并完成国家自然科学基金项目1项。主持国家质量工程特色专业项目“计算机科学与技术”。获发明专利2项,获软件著作权2项。发表学术论文80余篇,其中SCI、EI、ISTP收录30多篇,出版专著1部;鲍媛媛,女,1990年生,硕士研究生,主要研究方向为社交网络;赵姝,女,1979年生,副教授,博士,主要研究方向为商空间理论、粒度计算、机器学习等。主持安徽省高等学校优秀青年人才基金重点项目1项。申请专利3项,其中已获专利号1项,获软件著作权1项,发表学术论文10余篇。
通讯作者:赵姝.E-mail:zhaoshuzs@163.com.
更新日期/Last Update: 2015-08-28