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| A Network Evolution Model for Domain Knowledge Driven by Multiple Factors: Following Suit, Conservatism, and Innovation |
| Chen Guo1,2, Zhao Yixin3 |
1.Department of Information Management, Nanjing University of Science and Technology, Nanjing 210094
2.Jiangsu Science and Technology Collaborative Innovation Center of Social Public Safety, Nanjing210094
3.Institute of Software, Chinese Academy of Sciences, Beijing 100190 |
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Abstract Current complex network models, such as Barabasi-Albert scale-free models and Watts-Strogatz small-world networks, cannot imitate many structural characteristics of the real domain knowledge network. Therefore, it is necessary to explore a new network model suitable for the evolution of domain knowledge. Based on the example of a keyword network, this study discusses the process of domain knowledge generation and its multiple influencing factors. The study then proposes a new network evolution model with keyword modules as basic units, which comprehensively considers following suit, conservatism, and innovation as influencing factors. A simulation experiment showed that the model was effective in fitting the structural characteristics of domain knowledge networks at both macro and micro levels. A further simulation experiment revealed the impacts of following suit and conservatism on growth of new knowledge and knowledge gathering. The proposed model could provide quantitative foundations for future studies on domain knowledge analysis.
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Received: 25 September 2018
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