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圈比作为一种基于圈结构的量化指标，已在无权无向网络中展现出其在识别关键节点方面的显著优势。传统的圈比未能充分考虑边权信息对网络结构的影响，限制了其在更广泛网络分析中的应用。为了解决这一问题，本文提出了一种加权网络中新的网络分析指标——加权圈比，旨在提升识别加权网络中关键节点的准确性。通过对示例网络的分析，验证了加权圈比的可行性；进一步的实验在多个真实世界的网络中表明，加权圈比不仅与现有的基准指标存在显著差异，而且在评估网络连通性及早期传播覆盖范围方面，总体表现优于包括传统圈比在内的其他基准指标。这些发现强调了加权圈比在网络分析中的潜在价值，尤其是在处理加权网络时的有效性.In the face of surging air transportation demands and increasingly intense flight conflict risks, effectively managing flight conflicts and accurately identifying key conflicting aircraft have become critically important. This paper presents a novel method for identifying critical nodes in flight conflict networks by integrating complex network theory with a weighted cycle ratio (WCR). By modeling aircraft as nodes and conflict relationships as edges, we construct a flight conflict network where the urgency of conflicts is reflected in edge weights. We extend the traditional cycle ratio (CR) concept to propose the WCR, which accounts for both the topological structure of the network and the urgency of conflicts. Furthermore, we combine the WCR with node strength (NS) to form an adjustable mixed indicator (MI), which adaptively balances the importance of nodes based on their involvement in cyclic conflict structures and their individual conflict intensity. Through extensive simulations, including node deletion experiments and network robustness analyses, we demonstrate that our method can precisely pinpoint critical nodes in flight conflict networks. The results indicate that regulating these critical nodes can significantly reduce network complexity and conflict risks. Importantly, our method's effectiveness grows with the complexity of the flight conflict network, making it especially suitable for scenarios with high aircraft densities and intricate conflict patterns. Overall, this study not only advances the theoretical understanding of complex network analysis in aviation but also offers a practical tool for enhancing air traffic control efficiency and safety, ultimately contributing to greener and more sustainable air transportation.
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Keywords:
- Complex network /
- Cycle ratio /
- Weighted cycle ratio /
- vital node
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[1] Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D U 2006Phys. Rep. 424 175
[2] Lü L, Chen D, Ren X L, Zhang Q M, Zhang Y C, Zhou T 2016Phys. Rep. 650 1
[3] Yang M, Seklouli A S, Zhang H, Ren L, Yu X, Ouzrout Y 2023Proceedings of the 2023 International Conference on Computer Applications Technology Guiyang,China, September 15-17, 2023 p97
[4] Albert R, Albert I, Nakarado G L 2004 Phys. Rev. E 69 025103
[5] Easley D, Kleinberg J 2010IEEE Technology and Society Magazine.32 3
[6] Freeman L C 1978Soc. Networks 1 215
[7] Bonacich P 1972J. Math. Sociol. 2 113
[8] Freeman L C 1977Sociometry 40 35
[9] Liu C，Li D D，Han L，An Y X 2019Application Research of Computers 1 4（in Chinese）[刘臣, 李丹丹, 韩林, 安永雪2019计算机应用研究1 4]
[10] J Hu, B Wang, D Lee 2010IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing Hangzhou, China, December 18-20, 2010 p792
[11] Zhang G H, Liu W, Wang R X Y, Li X P, Gong Z C,Chen Y Y, Chen H Y 2023Wireless Internet Technology 6 116（in Chinese） [张格豪,刘伟,王睿鑫垚,厉鑫鹏,龚子忱,陈一源,陈海洋,.2023无线互联科技, 6 116]
[12] Lambiotte R, Rosvall M 2019 Nat. Phys. 15 313
[13] Perera S, Bell M G, Bliemer M C 2017Appl. Netw. Sci. 2, 33
[14] Battiston F, Cencetti G, Iacopini I, Latora V, Lucas M, Patania A, Young J G, Petri G 2020Phys. Rep. 874 1
[15] Song J, Wang Y, Xu G 2024Comput. Netw. 220 108969
[16] Shi D, Lu L, Chen G 2019Natl. Sci. Rev. 6 962
[17] Shi D, Chen G, Thong W W K, Yan X Y 2013IEEE Circuits Syst. Mag. 13 66
[18] Lou Y, Wang L, Chen G 2018 IEEE Trans. Circuits Syst. I Regul. Pap. 65 983
[19] Sizemore A E, Giusti C, Kahn A, Vettel J M, Betzel R F, Bassett D S 2017J. Comput. Neurosci. 44 115
[20] Watts D J, Strogatz S H 1998Nature 393 440
[21] Fronczak A, Hołst J A, Jedynak M, Sienkiewicz J 2002Physica A 316 688
[22] Caldarelli G, Pastor-Satorras R, Vespignani A 2004Eur. Phys. J. B 38 183
[23] Kim H J, Kim J M 2005 Phys. Rev. E 72 036109
[24] Fan T, Lü L, Shi D et al 2021Commun. Phys. 4272
[25] Croft D P, James R, Krause J 2008Exploring Animal Social Networks (Princeton: Princeton University Press)pp1-18
[26] Cha M, Haddadi H, Benevenuto F, Gummadi K P 2010Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media Washington, DC, USA, May 23-26, 2010 p10.
[27] Guimerà R, Mossa S, Turtschi A, Amaral L A N 2005Proc. Natl. Acad. Sci. U.S.A. 102 7794
[28] Kossinets G, Watts D J 2006Science 311 88
[29] Yang H, Bell M G H 1998Transp. Rev. 18 257
[30] Lü J, Zhang B, Zhou T 2015Physica A 418 65
[31] Helander M, Kertész J 2021EPJ Data Sci. 11 1
[32] Noschese S, Reichel L 2024Numer. Algor. 95451
[33] Zhang J, Liu X 2022J. Comput. Sci. 60101591
[34] Kendall M 1938Biometrika 30 81
[35] Callaway D S, Newman M E J, Strogatz S H, Watts D J 2000Phys. Rev. Lett. 85 5468
[36] Cohen R, Erez K, Ben-Avraham D, Havlin S 2001Phys. Rev. Lett. 863682
[37] Tian L, Di Z R, Yao H 2011Acta Phys. Sin. 60803（in Chinese）[田柳, 狄增如, 姚虹2011物理学报60 803]
[38] Pastor-Satorras R, Castellano C, Van Mieghem P, Vespignani A 2015Rev. Mod. Phys. 87 925
[39] Gubar E, Zhu Q, Taynitskiy V 2017Proceedings of the 7th EAI International Conference on Game Theory for Networks Knoxville, Tennessee, USA, May 9, 2017 p108
[40] Zhou F, Lü L, Mariani M S 2019 Commun. Nonlinear Sci. Numer. Simul. 74 69
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