Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective
Abstract
:1. Introduction
2. ET-TIN Construction and Measurement
2.1. ET-TIN Construction
2.2. ET-TIN Measurement
2.2.1. Network Scale
2.2.2. Scale-Free Characteristic of Network
2.2.3. Small-World Characteristic of Network
2.2.4. Self-Organizing Characteristic of Network
3. The Modeling of ET-TIN Evolution
3.1. Network Nodes Evolution Mechanism
3.1.1. Nodes Joining Mechanism
3.1.2. Nodes Retaining and Exiting Mechanism
3.2. Network Edges Evolution Mechanism
3.2.1. Preferential Attachment Mechanism Based on Knowledge Novelty
3.2.2. Preferential Attachment Mechanism Based on Knowledge Coherence
3.2.3. Preferential Attachment Mechanism Based on Knowledge Growth
3.2.4. Preferential Attachment Mechanism Based on Knowledge Influence
3.2.5. Preferential Attachment Mechanism Based on Multiple Knowledge Attributes
3.3. System Framework of Network Evolution
4. The Simulation Design of ET-TIN Evolution
4.1. Data Sources
4.2. Simulation Steps
5. Results and Discussion
5.1. Analysis of Network Scale
5.2. Analysis of Scale-Free Characteristic of Network
5.3. Analysis of Small-World Characteristic of Network
5.4. Analysis of Self-Organizing Characteristic of Network
6. Conclusions
6.1. Summary
6.2. Suggestions
6.3. Limitations and Future Research Directions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Notations | Description |
---|---|
Level Ⅰ | |
The probability of node being connected preferentially based on its multiple knowledge attributes. | |
Level Ⅱ | |
The probability of node being connected preferentially based on its knowledge novelty. | |
The probability of node being connected preferentially based on its knowledge coherence. | |
The probability of node being connected preferentially based on its knowledge growth. | |
The probability of node being connected preferentially based on its knowledge influence. | |
The fitness of node , and there is no unified standard for the probability distribution of node fitness, which should be determined according to the real network data. | |
The upper limit of knowledge absorption of node , which can be set to obey the power law distribution according to the in-degree distribution of nodes in most real networks. | |
Level Ⅲ | |
The knowledge novelty of node at time , which is used to calculate . | |
The knowledge structure vector formed by the innovation outputs of node in different knowledge domains up to time since they joined the network, which is used to calculate . | |
The in-degree of node at time , which can reflect its knowledge absorption and be used to calculate . | |
The out-degree of node at time , which can reflect its knowledge diffusion and be used to calculate . |
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Liu, Y.; Chen, Y.; He, Q.; Yu, Q. Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective. Systems 2023, 11, 82. https://doi.org/10.3390/systems11020082
Liu Y, Chen Y, He Q, Yu Q. Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective. Systems. 2023; 11(2):82. https://doi.org/10.3390/systems11020082
Chicago/Turabian StyleLiu, Yaqin, Yunsi Chen, Qing He, and Qian Yu. 2023. "Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective" Systems 11, no. 2: 82. https://doi.org/10.3390/systems11020082