作　　者： (李文兰）; (王野）; (李立）; (郑彩雪）;

机构地区： 天津大学图书馆,天津300072 天津大学情报研究所,天津300072

出　　处： 《情报理论与实践》 2017年第9期95-100,共6页

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。 [ Purpose/significance ] With regards to the insufficient application of indexes identified by key nodes in collaboration network, this paper proposes a new method to recognize the key nodes in collaboration network based on multi-attribute deci- sion-making TOPSIS theory. [ Method/process ] Firstly, degree centrality, closeness centrality and eigenvector centrality are chosen as the multi-attribute decision-making indexes based on the types of information flow in collaboration network. Secondly, the index weight is calculated based on entropy-weight theory. At last, the paper uses the TOPSIS method to identify the key nodes in collaboration network, and then makes an empirical research on the author collaboration network of articles published on the journal of ＂Scientometrics＂ in 2011 - 2015. [ Result/conclusion] The key authors like G. Abramo and C. A. D＇ Angelo, are successfully identified through TOPSIS theory, and the feasibility of this model is verified through the SI model and deleting nodes.

关 键 词： 合著网络 节点属性 识别 决策