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, DOI：10.11925/infotech.1003-3513.2015.04.09.
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