Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted Network
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-weighte...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8704247/ |
| _version_ | 1818416007747731456 |
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| author | Qiaojiayu Wang Dejiang Wang Gengyuan Bai Qian Yu |
| author_facet | Qiaojiayu Wang Dejiang Wang Gengyuan Bai Qian Yu |
| author_sort | Qiaojiayu Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T11:44:03Z |
| format | Article |
| id | doaj.art-be837810f5e545de92f4494c9c88c941 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:44:03Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-be837810f5e545de92f4494c9c88c9412022-12-21T23:02:41ZengIEEEIEEE Access2169-35362019-01-017600266004110.1109/ACCESS.2019.29142348704247Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted NetworkQiaojiayu Wang0Dejiang Wang1https://orcid.org/0000-0002-1462-2156Gengyuan Bai2Qian Yu3School of Economics, Wuhan University of Technology, Wuhan, ChinaSchool of Economics, Wuhan University of Technology, Wuhan, ChinaSchool of Economics, Wuhan University of Technology, Wuhan, ChinaSchool of Economics, Wuhan University of Technology, Wuhan, ChinaThe 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.https://ieeexplore.ieee.org/document/8704247/Co-occurrence analysisuser innovationtemporal-weighted user innovation knowledge mapcollaborative creativity |
| spellingShingle | Qiaojiayu Wang Dejiang Wang Gengyuan Bai Qian Yu Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted Network IEEE Access Co-occurrence analysis user innovation temporal-weighted user innovation knowledge map collaborative creativity |
| title | Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted Network |
| title_full | Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted Network |
| title_fullStr | Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted Network |
| title_full_unstemmed | Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted Network |
| title_short | Co-Occurrence and Cyclical Growth Law Analysis of User Innovation Knowledge Map Based on Temporal-Weighted Network |
| title_sort | co occurrence and cyclical growth law analysis of user innovation knowledge map based on temporal weighted network |
| topic | Co-occurrence analysis user innovation temporal-weighted user innovation knowledge map collaborative creativity |
| url | https://ieeexplore.ieee.org/document/8704247/ |
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