[1]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(4):620-626.[doi:10.3969/j.issn.1673-4785.201505052]
Copy

Dynamic microscopic structure evolution analysis for the author collaboration network of academic conferences

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 4 Page number: 620-626 Column: 学术论文—机器感知与模式识别 Public date: 2015-08-25
Title:
Dynamic microscopic structure evolution analysis for the author collaboration network of academic conferences
Author(s):
ZHANG Yanping1; 2;  BAO Yuanyuan1; 2;  ZHAO Shu1; 2;  CHEN Jie1; 2;  HUANG Menghan1; 2
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
Keywords:
evolution; author collaboration network; opinion leader; structural hole; microscopic structure; PageRank; academic conferences; number of citations
CLC:
TP399
DOI:
10.3969/j.issn.1673-4785.201505052
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.
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/.
Similar References:

Memo

-

Last Update: 2015-08-28

Copyright © CAAI Transactions on Intelligent Systems