Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted Network

被引：3

作者：

Wang, Qiaojiayu ^{[1
]}

Wang, Dejiang ^{[1
]}

Bai, Gengyuan ^{[1
]}

Yu, Qian ^{[1
]}

机构：

[1] Wuhan Univ Technol, Sch Econ, Wuhan 430070, Hubei, Peoples R China

来源：

IEEE ACCESS | 2019年 / 7卷

基金：

中国国家自然科学基金;

关键词：

Co-occurrence analysis; user innovation; temporal-weighted user innovation knowledge map; collaborative creativity; SOCIAL NETWORKS; DYNAMICS;

D O I：

10.1109/ACCESS.2019.2914234

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The construction of user innovation knowledge map can clearly describe the co-occurrence relationship among innovative knowledge points. Different types of weighted knowledge map models have been applied by enterprises to promote their innovative knowledge development. Moreover, the temporal-weighted user innovation knowledge map can reveal the hidden hot knowledge network model accurately and reflect the general evolution law of the hot topic of innovation knowledge in a certain period. In this paper, we proposed a method based on a knowledge network for constructing the temporal-weighted user innovation knowledge map. In order to model the temporal-weighted co-occurrence relationship among knowledge points, we assigned different weights to the knowledge points at different periods. Then, we carried out centrality analysis and core subgroup analysis for knowledge points through using a time-dynamic analysis method to explore the dynamic laws. Based on that, we proposed the periodical growth rule of user innovation hotspots. It was convenient to predict the hot spots of innovation and grasp the pace of innovation. Besides, we conducted an empirical study of the "Arduino" community, which is the most professional electronic enthusiast community in China. We applied the established model to explore the sensitivity and accuracy of the knowledge map after adding the temporal factor to the user innovation knowledge map, and we found out the evolution process of co-occurrence relationship between user innovation knowledge points. Finally, we concluded that it takes time to ultimately establish a core-edge innovation knowledge model to represent specific innovation fields. More importantly, the development of hotspots of innovative knowledge presented the general growth cycle of the four stages: the initial stage, the explosive development stage, the mature stage, and the recession stage. Discovery of this law has a major impact on the company's trending of the future "frontier research" field and the entry choice of innovation.

引用

页码：60026 / 60041

页数：16

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