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
[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.
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, 2015, 31(4): 65-71.
 Newman M E J. Coauthorship Networks and Patterns of Scientific Collaboration [J]. Proceedings of the National Academy of Sciences, 2004, 101(S1): 5200-5205.
 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.
 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.
 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.
 Ahuja G. Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study [J]. Administrative Science Quarterly, 2000, 45(3): 425-455.
 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.
 Lü L, Zhou T. Link Prediction in Complex Networks: A Survey [J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(6): 1150-1170.
 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.
 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.
 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.
 Newman M E J. Communities, Modules and Large-scale Structure in Networks [J]. Nature Physics, 2012, 8(1): 25-31.
 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.
 Vapnik V N, Vapnik V. Statistical Learning Theory [M]. New York: Wiley, 1998.
 Newman M E J. Modularity and Community Structure in Networks [J]. Proceedings of the National Academy of Sciences, 2006,103(23): 8577-8582.
 Shi J, Malik J. Normalized Cuts and Image Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
 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.