[Objective] This paper aims to reveal the common structural features of keyword network of scientific research areas both at the macro level and micro level.[Methods] Three keyword networks are constructed. Theirmacro feature properties are compared with ER network, BA network and SW network, and regression analysis on theirmicro feature properties are performed.[Results] The degree sequence of keyword network shows a power-law distribution, the average clustering coefficient of them is extremely high and the average path length of them is short.The degree, betweenness centrality, eigenvector centrality, and triad closure of nodes and the frequency of keywords have positive linear correlations, while there is an inverse relationship between the local clustering coefficient of nodesand their degree.[Limitations] Samples need to be expanded to more other disciplines.[Conclusions] The keyword network of scientific research areas are special scale-free networks with small world effect, modularity, hierarchy and high centripetalism.
陈果, 胡昌平. 科研领域关键词网络的结构特征与启示-基于图情学科的实证研究[J]. 现代图书情报技术, 2014, 30(7): 84-91.
Chen Guo, Hu Changping. Research on the Structural Features of Keyword Network of Scientific Research Areas:An Empirical Study of LIS. New Technology of Library and Information Service, 2014, 30(7): 84-91.
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