[Purpose/significance] Under the condition of guaranteeing the whole character of knowledge networks, this paper aims to extract a significant knowledge networks at level from the original knowledge networks, in order to establish the theoretical basis of the knowledge network at level extracted by correlation frequency. [Method/process] Under the basic theory support of scale-free network and fractal geometry, based on the analysis on the correlation frequency distribution of keywords knowledge network and tags knowledge network, this article extracts the knowledge networks at level using the correlation frequency as the threshold. The scale-free and small-world effect of the knowledge networks at level are verified. [Result/conclusion] The correlation frequency distributions of the knowledge networks match power-law distribution. The knowledge networks at level, which take the correlation frequency as the threshold, keep the scale-free of whole original networks at nodes degree distribution and correlation frequency distribution. The knowledge networks at level can keep the small-world characteristics of the original network well. The knowledge network at level based on correlation frequency is equivalent to the original knowledge network.
Teng Guangqing, Bai Shuchun, Han Shangxuan, Bi Qiang
. Analysis on the Principle of Knowledge Network at Level Based on Scale-free and Fractal Theory[J]. Library and Information Service, 2017
, 61(14)
: 132
-140
.
DOI: 10.13266/j.issn.0252-3116.2017.14.018
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