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现代图书情报技术  2015, Vol. 31 Issue (4): 65-71    DOI: 10.11925/infotech.1003-3513.2015.04.09
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
混合拓扑因子的科研网络合作关系预测
伍杰华1,2, 朱岸青1,3
1 广东工贸职业技术学院计算机工程系 广州 510510;
2 华南理工大学计算机科学与工程学院 广州 510641;
3 暨南大学信息科学技术学院 广州 510632
Mixture Topological Factors for Collaboration Prediction in Academic Network
Wu Jiehua1,2, Zhu Anqing1,3
1 Computer Engineering Department, Guangdong College of Industry and Commerce, Guangzhou 510510, China;
2 School of Computer Science & Engineering, South China University of Technology, Guangzhou 510641, China;
3 College of Information Science and Technology, Jinan University, Guangzhou 510632, China
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摘要 

[目的]通过图论和复杂网络理论中的链接(关系)预测算法挖掘科研合作网络的结构信息, 并预测目前尚未合作的学者有哪些在未来会产生合作关系。[方法]提出一种新颖的集成局部拓扑特征因子和全局社区拓扑特征的混合拓扑因子合作关系预测模型(Mixture Topological Factor, MTF), 该模型引入朴素贝叶斯模型关系预测算法计算局部因子, 采用社区贡献度和参与度计算全局社区特征因子进行集成。[结果]实验结果表明, MTF方法能够在采用不同社区算法的基础上有效地对真实的科研合作网络关系预测问题建模, 在效果上也要优于一些经典和新近提出的算法。[局限]该方法有待进一步应用到更大规模的网络结构中。[结论]能够通过深入挖掘科研合作网络基于社区信息的拓扑属性提高预测精确度, 同时为该类模型的研究提供一种新的方案。

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伍杰华
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关键词 科研网络合作关系合作预测学者社区社区信息混合拓扑因子模型    
Abstract

[Objective] The paper aims to predict the cooperation between scholars according to the academic research network's structural information. [Methods] A novel mixture topological factor predictive model called MTF is proposed, which cooperating local feature factors and global community factors. This model firstly introduces Naïve Bayesian algorithm to calculate local factors and then uses community contribution to compute the global factors. [Results] Experimental results show that MTF method can effectively handle the task of real scientific collaboration network relationships prediction, also performs better than some of the classic and newly proposed algorithms. [Limitations] The data used in the experiments should be at a larger scale. [Conclusions] This paper proves that the proposed model can mine community information for improving prediction performance, which leads to a new path in such area.

Key wordsAcademic network    Collaboration relation    Collaboration prediction    Scholar community    Community information    Mixture topological factor model
收稿日期: 2014-09-22     
:  G202  
基金资助:

本文系国家自然科学基金青年基金项目“半监督排序学习理论与算法研究”(项目编号:61003045)和广东省教育部产学研结合项目“基于节能的企业能耗监控集成管理平台研发及应用”(项目编号:2012B091100043)的研究成果之一。

通讯作者: 伍杰华,ORCID:0000-0003-2925-2300,E-mail:kodakwu@126.com     E-mail: kodakwu@126.com
作者简介: 作者贡献声明: 伍杰华:提出研究思路,设计研究方案,进行实验并起草论文;朱岸青:采集、清洗和分析数据,论文最终版本修订。
引用本文:   
伍杰华, 朱岸青. 混合拓扑因子的科研网络合作关系预测[J]. 现代图书情报技术, 2015, 31(4): 65-71.
Wu Jiehua, Zhu Anqing. Mixture Topological Factors for Collaboration Prediction in Academic Network. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.04.09.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.04.09

[1] Newman M E J. Coauthorship Networks and Patterns of Scientific Collaboration [J]. Proceedings of the National Academy of Sciences, 2004, 101(S1): 5200-5205.
[2] Zhang P, Chen K, He Y, et al. Model and Empirical Study on Some Collaboration Networks [J]. Physica A: Statistical Mechanics and Its Applications, 2006, 360(2): 599-616.
[3] Barabasi A L, 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] Xu M, Zhu J, Zhang B. Nonparametric Max-margin Matrix Factorization for Collaborative Prediction [C]. In: Proceedings of Advances in Neural Information Processing Systems. 2012:64-72.
[5] Ahuja G. Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study [J]. Administrative Science Quarterly, 2000, 45(3): 425-455.
[6] Liben-Nowell D, Kleinberg J. The Link Prediction Problem for Social Networks [J]. Journal of the American Society for Information Science and Technology, 2007, 58(7): 1019-1031.
[7] Lü L, Zhou T. Link Prediction in Complex Networks: A Survey [J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(6): 1150-1170.
[8] Tang J, Wu S, Sun J, et al. Cross-domain Collaboration Recommendation [C]. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'12). New York: ACM, 2012: 1285-1293.
[9] Sun Y, Barber R, Gupta M, et al. Co-author Relationship Prediction in Heterogeneous Bibliographic Networks[C]. In: Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM'11). Washington D C: IEEE Computer Society, 2011: 121-128.
[10] Sun Y, Han J, Aggarwal C C, et al. When Will It Happen?: Relationship Prediction in Heterogeneous Information Networks [C]. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM'12). New York: ACM, 2012: 663-672.
[11] Newman M E J. Communities, Modules and Large-scale Structure in Networks [J]. Nature Physics, 2012, 8(1): 25-31.
[12] Liu Z, Zhang Q M, Lü L, et al. Link Prediction in Complex Networks: A Local Naïve Bayes Model [J]. EPL (Europhysics Letters), 2011, 96(4). doi: 10.1209/0295-5075/96/48007.
[13] Vapnik V N, Vapnik V. Statistical Learning Theory [M]. New York: Wiley, 1998.
[14] Newman M E J. Modularity and Community Structure in Networks [J]. Proceedings of the National Academy of Sciences, 2006,103(23): 8577-8582.
[15] Shi J, Malik J. Normalized Cuts and Image Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
[16] Kunegis J. KONECT: The Koblenz Network Collection [C]. In: Proceedings of the 22nd International Conference on World Wide Web Companion (WWW'13). 2013: 1343-1350.

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