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[目的/意义]针对网络结构关键节点识别指标在合著网络应用中存在的不足,基于多属性决策TOPSIS理论构建了识别合著网络关键节点的新方法。[方法/过程]首先基于合著网络信息流类型选择度中心性、接近中心性和特征向量中心性为多属性决策评价指标;其次基于熵权理论计算出各指标权重;最后通过多属性决策TOPSIS方法识别出合著网络中关键节点,并以“Scientometrics”期刊2011—2015年论文作者合著网络进行了实证研究。[结果/结论]基于多属性决策TOPSIS方法识别出了G.Abramo和C.A.D’Angelo等关键作者;并基于传染病SI模型思想和节点删除思想验证了多属性决策TOPSIS方法的有效性。
[Purpose / Significance] Aiming at the shortcomings of the key node identification index of network structure in co-operation network application, a new method to identify the key nodes of co-operation network is constructed based on multi-attribute decision-making TOPSIS theory. [Method / Process] Firstly, based on co-author network information flow, the type centroid, centroid and eigenvector centrality are evaluated as multi-attribute decision making indicators. Secondly, the weight of each index is calculated based on entropy theory. Finally, Method is used to identify the key nodes in the co-author network, and the empirical research is carried out in the co-author network of “Scientometrics” 2011-2015 essay. [Result / Conclusion] Key authors of G.Abramo and C.A.D’Angelo were identified based on TOPSIS method. The validity of multi-attribute decision-making TOPSIS method was validated based on SI model and node deletion idea.