Study on the Complexity Evolution of the Aviation Network in China
As China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressi...
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MDPI AG
2025-07-01
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| author | Shuolei Zhou Cheng Li Shiguo Deng |
| author_facet | Shuolei Zhou Cheng Li Shiguo Deng |
| author_sort | Shuolei Zhou |
| collection | DOAJ |
| description | As China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressing critical gaps in prior static network analyses. Unlike conventional studies focusing on isolated topological metrics, we introduce a triangulated methodology: ① a network sequence analysis capturing structural shifts in degree distribution, clustering coefficient, and path length; ② novel redundancy–entropy coupling quantifying complexity evolution beyond traditional efficiency metrics; and ③ economic-network coordination modeling with spatial autocorrelation validation. Key innovations reveal previously unrecognized dynamics: ① Time-embedded density matrices (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ρ</mi></mrow></semantics></math></inline-formula>) demonstrate how sparsity balances information propagation efficiency (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>η</mi></mrow></semantics></math></inline-formula>) and response diversity, resolving the paradox of functional yet sparse connectivity. ② Redundancy–entropy synergy exposes adaptive trade-offs. Entropy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>H</mi></mrow></semantics></math></inline-formula>) rises 18% (2000–2024), while redundancy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi></mrow></semantics></math></inline-formula>) rebounds post-2010 (0.25→0.33), reflecting the strategic resilience enhancement after early efficiency-focused phases. ③ Economic-network coupling exhibits strong spatial autocorrelation (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>o</mi><mi>r</mi><mi>a</mi><mi>n</mi><mo>’</mo><mi>s</mi><mtext> </mtext><mi>I</mi><mo>></mo><mn>0.16</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.05</mn></mrow></semantics></math></inline-formula>), with eastern China achieving “primary coordination”, while western regions lag due to geographical constraints. The empirical results confirm structural self-organization. Power-law strengthening, route growth exponentially outpacing cities, and clustering (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi></mrow></semantics></math></inline-formula>) rising 16% as the path length (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi></mrow></semantics></math></inline-formula>) increases, validating the hierarchical hub formation. These findings establish aviation networks as dynamically optimized systems where economic policies and topological laws interactively drive evolution, offering a paradigm shift from descriptive to predictive network management. |
| format | Article |
| id | doaj-art-ec28a95bd4f44ddfb9d72f2d6e552ff3 |
| institution | Matheson Library |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-ec28a95bd4f44ddfb9d72f2d6e552ff32025-07-25T13:37:07ZengMDPI AGSystems2079-89542025-07-0113756310.3390/systems13070563Study on the Complexity Evolution of the Aviation Network in ChinaShuolei Zhou0Cheng Li1Shiguo Deng2College of Air Transportation, Shanghai University of Engineering Science, Shanghai 201600, ChinaCollege of Air Transportation, Shanghai University of Engineering Science, Shanghai 201600, ChinaCollege of Air Transportation, Shanghai University of Engineering Science, Shanghai 201600, ChinaAs China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressing critical gaps in prior static network analyses. Unlike conventional studies focusing on isolated topological metrics, we introduce a triangulated methodology: ① a network sequence analysis capturing structural shifts in degree distribution, clustering coefficient, and path length; ② novel redundancy–entropy coupling quantifying complexity evolution beyond traditional efficiency metrics; and ③ economic-network coordination modeling with spatial autocorrelation validation. Key innovations reveal previously unrecognized dynamics: ① Time-embedded density matrices (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ρ</mi></mrow></semantics></math></inline-formula>) demonstrate how sparsity balances information propagation efficiency (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>η</mi></mrow></semantics></math></inline-formula>) and response diversity, resolving the paradox of functional yet sparse connectivity. ② Redundancy–entropy synergy exposes adaptive trade-offs. Entropy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>H</mi></mrow></semantics></math></inline-formula>) rises 18% (2000–2024), while redundancy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi></mrow></semantics></math></inline-formula>) rebounds post-2010 (0.25→0.33), reflecting the strategic resilience enhancement after early efficiency-focused phases. ③ Economic-network coupling exhibits strong spatial autocorrelation (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>o</mi><mi>r</mi><mi>a</mi><mi>n</mi><mo>’</mo><mi>s</mi><mtext> </mtext><mi>I</mi><mo>></mo><mn>0.16</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.05</mn></mrow></semantics></math></inline-formula>), with eastern China achieving “primary coordination”, while western regions lag due to geographical constraints. The empirical results confirm structural self-organization. Power-law strengthening, route growth exponentially outpacing cities, and clustering (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi></mrow></semantics></math></inline-formula>) rising 16% as the path length (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi></mrow></semantics></math></inline-formula>) increases, validating the hierarchical hub formation. These findings establish aviation networks as dynamically optimized systems where economic policies and topological laws interactively drive evolution, offering a paradigm shift from descriptive to predictive network management.https://www.mdpi.com/2079-8954/13/7/563network sequence analysisredundancy–entropy couplingaviation network evolutiontime-embedded density matrixeconomic-network coordination |
| spellingShingle | Shuolei Zhou Cheng Li Shiguo Deng Study on the Complexity Evolution of the Aviation Network in China Systems network sequence analysis redundancy–entropy coupling aviation network evolution time-embedded density matrix economic-network coordination |
| title | Study on the Complexity Evolution of the Aviation Network in China |
| title_full | Study on the Complexity Evolution of the Aviation Network in China |
| title_fullStr | Study on the Complexity Evolution of the Aviation Network in China |
| title_full_unstemmed | Study on the Complexity Evolution of the Aviation Network in China |
| title_short | Study on the Complexity Evolution of the Aviation Network in China |
| title_sort | study on the complexity evolution of the aviation network in china |
| topic | network sequence analysis redundancy–entropy coupling aviation network evolution time-embedded density matrix economic-network coordination |
| url | https://www.mdpi.com/2079-8954/13/7/563 |
| work_keys_str_mv | AT shuoleizhou studyonthecomplexityevolutionoftheaviationnetworkinchina AT chengli studyonthecomplexityevolutionoftheaviationnetworkinchina AT shiguodeng studyonthecomplexityevolutionoftheaviationnetworkinchina |