기술융합 구조 변화와 융합 예측에 관한 연구 : ICT 원천 분야 R&D 특허 성과를 중심으로
A study on Technology Convergence Structure Variability and Convergence Prediction : Focused on R&D patent performance in ICT Original Technology
최가영(서울과학기술대학교 IT정책전문대학원); 조남욱(서울과학기술대학교)
28권 3호, 47~59쪽
초록
Purpose The purpose of this study is to compare and analyze technology convergence trend analysis and technology convergence prediction performance of ‘SW original R&D' and `HW(device) original R&D.' Methods QAP(Quadratic Assignment Procedure) for network relevance measurement and graph auto-encoder model for link prediction have been used in this study. Results ‘SW original R&D' shows greater structural variability in the technology convergence network than ‘HW(device) original R&D.' In terms of link characteristics between IPC nodes, ‘HW(device) original R&D' shows more significant variability in the technology convergence network than ‘SW original R&D.' The link prediction performance using graph auto-encoder is higher for ‘SW' than ‘Device.' Conclusion The variability of node-to-node connection characteristics has a more significant impact on link prediction performance than the variability of network structure. In addition, this study shows that for technology convergence analysis and link prediction, it is necessary to examine not only the structural characteristics of the network but also the connection status and characteristics based on the interrelationships between entities in the network.
Abstract
Purpose The purpose of this study is to compare and analyze technology convergence trend analysis and technology convergence prediction performance of ‘SW original R&D' and `HW(device) original R&D.' Methods QAP(Quadratic Assignment Procedure) for network relevance measurement and graph auto-encoder model for link prediction have been used in this study. Results ‘SW original R&D' shows greater structural variability in the technology convergence network than ‘HW(device) original R&D.' In terms of link characteristics between IPC nodes, ‘HW(device) original R&D' shows more significant variability in the technology convergence network than ‘SW original R&D.' The link prediction performance using graph auto-encoder is higher for ‘SW' than ‘Device.' Conclusion The variability of node-to-node connection characteristics has a more significant impact on link prediction performance than the variability of network structure. In addition, this study shows that for technology convergence analysis and link prediction, it is necessary to examine not only the structural characteristics of the network but also the connection status and characteristics based on the interrelationships between entities in the network.
- 발행기관:
- 한국경영공학회
- 분류:
- 산업공학