混合拓扑因子的科研网络合作关系预测
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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。
[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 Na?ve 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.
[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 Na?ve 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.
引文
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[12]Liu Z,Zhang Q M,LüL,et al.Link Prediction in Complex Networks:A Local Nave 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.
[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]LüL,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 InternationalConference 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,LüL,et al.Link Prediction in Complex Networks:A Local Nave 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.