基于多属性决策合著网络关键节点识别研究

发布时间：2018-06-28 22:04

本文选题：合著网络 + 节点属性　；参考：《情报理论与实践》2017年09期

【摘要】：[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。
[Abstract]:[objective / significance] in view of the shortcomings of the key node identification index of network structure in the application of co-authoring network, a new method to identify the key nodes of coauthor network is constructed based on the theory of multi-attribute decision making (TOPSIS). [method / process] firstly, based on the centrality of type selection of coauthor network information flow, proximity and characteristic vector centrality are the evaluation indexes of multi-attribute decision making, and then the weights of each index are calculated based on entropy weight theory. Finally, the key nodes in co-authorship network are identified by TOPSIS method of multi-attribute decision making, and an empirical study is carried out with the co-author network of Scientometrics in 2011-2015. [results / conclusion] based on the TOPSIS method of multi-attribute decision making, the key authors of G. Abramo and C.A. DX Angelo are identified, and the effectiveness of the TOPSIS method based on SI model of infectious diseases and node deletion is verified.
【作者单位】：天津大学图书馆;天津大学情报研究所;天津大学管理与经济学部;
【分类号】：G353.1

【相似文献】