Please wait a minute...
Chin. Phys. B, 2020, Vol. 29(9): 096401    DOI: 10.1088/1674-1056/aba275
CONDENSED MATTER: STRUCTURAL, MECHANICAL, AND THERMAL PROPERTIES Prev   Next  

Analysis of overload-based cascading failure in multilayer spatial networks

Min Zhang(张敏)1,2, Xiao-Juan Wang(王小娟)2,3,4, Lei Jin(金磊)2, Mei Song(宋梅)2, Zhong-Hua Liao(廖中华)3,4
1 School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2 School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
3 Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing 100854, China;
4 State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, China
Abstract  Many complex networks in real life are embedded in space and most infrastructure networks are interdependent, such as the power system and the transport network. In this paper, we construct two cascading failure models on the multilayer spatial network. In our research, the distance l between nodes within the layer obeys the exponential distribution P(l)~exp(-l/ζ), and the length r of dependency link between layers is defined according to node position. An entropy approach is applied to analyze the spatial network structure and reflect the difference degree between nodes. Two metrics, namely dynamic network size and dynamic network entropy, are proposed to evaluate the spatial network robustness and stability. During the cascading failure process, the spatial network evolution is analyzed, and the numbers of failure nodes caused by different reasons are also counted, respectively. Besides, we discuss the factors affecting network robustness. Simulations demonstrate that the larger the values of average degree <k>, the stronger the network robustness. As the length r decreases, the network performs better. When the probability p is small, as ζ decreases, the network robustness becomes more reliable. When p is large, the network robustness manifests better performance as ζ increases. These results provide insight into enhancing the robustness, maintaining the stability, and adjusting the difference degree between nodes of the embedded spatiality systems.
Keywords:  cascading failure      multilayer network      load distribution      spatial network      entropy  
Received:  07 May 2020      Revised:  28 June 2020      Accepted manuscript online:  03 July 2020
PACS:  64.60.aq (Networks)  
  64.60.ah (Percolation)  
  89.75.Fb (Structures and organization in complex systems)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61871046).
Corresponding Authors:  Xiao-Juan Wang     E-mail:  wj2718@163.com

Cite this article: 

Min Zhang(张敏), Xiao-Juan Wang(王小娟), Lei Jin(金磊), Mei Song(宋梅), Zhong-Hua Liao(廖中华) Analysis of overload-based cascading failure in multilayer spatial networks 2020 Chin. Phys. B 29 096401

[1] Guo L Q, Liang C, Zocca A and Low S Proceedings of the 2018 IEEE Conference on Decision and Control, December 17-19, Florida, USA, p. 6832
[2] Wang C and Huang Z D 2019 Int. J. Mod. Phys. B 33 1950262
[3] Ren W D, Wu J J, Zhang X and Lai R 2018 IEEE Trans. Circuits Syst. 65 632
[4] Zeng Y and Xiao R B 2014 Int. J. Prod. Res. 52 6938
[5] Wang Y C and Zhang F P 2018 Nonlinear Dyn. 92 1403
[6] Zhang Z H, Yin Y F, Zhang X and Liu L J 2018 PLoS One 13 0192874
[7] Ahajjam S and Badir H 2018 Sci. Rep. 8 11932
[8] Tang L R, Yang Y, Fan B and Wu R Z 2018 Eur. Phys. J. B 91 288
[9] Wang H, Li M, Deng L and Wang B H 2018 Physica A 502 195
[10] Chen M, Song M, Zhang M, Jin L and Gong X Y 2019 Int. J. Mod. Phys. C 30 1
[11] Watts D J 2002 Proc. Natl. Acad. Sci. USA 99 5766
[12] Buldyrev S V, Parshani R, Paul G, Stanley H E and Havlin S 2010 Nature 463 1025
[13] Lee K M, Brummitt C D and Goh K I 2014 arXiv: 1403.3472 [physics.soc-ph]
[14] Gao J X, Buldyrev S V, Havlin S and Stanley H E 2012 Phys. Rev. E 85 066134
[15] Zhou D and Elmokashfi A 2018 Sci. Rep. 8 7433
[16] Jin L, Wang X J, Zhang Y and You J W 2018 Chin. Phys. B 27 098901
[17] Boccaletti S, Bianconi G, Criado R, Del Genio C I, Gómezgardeñes J, Romance M, Sendiñanadal I, Wang Z and Zanin M 2014 Phys. Rep. 544 1
[18] Li M and Wang B H 2014 Chin. Phys. B 23 076402
[19] Zhou J, Huang N, Coit D W and Felder F A 2018 Reliab. Eng. Syst. Safe. 170 116
[20] Tang L, Jing K, He J and Stanley H E 2016 Physica A 443 58
[21] Wang H, Shen H and Li Z Proceedings of the 38th IEEE International Conference on Distributed Computing Systems, July 2-6, Vienna, Austria, p. 706
[22] Prima M C, Duchesne T, Fortin A, Rivest L P, Drapeau P, Laurent M H and Fortin D 2019 Funct. Ecol. 00 1
[23] Varol C and Söylemez E 2018 Socio spatial network structures in border regions: west and east borders of turkey (Germany: Springer-Verlag) pp. 207-225
[24] Qian Y Q, Yang M, Zhao X and Wang C X 2019 IEEE Trans. Multimedia 22 421
[25] Li W, Bashan A, Buldyrev S V, Stanley H E and Havlin S 2012 Phys. Rev. Lett. 108 228702
[26] Shekhtman L M, Berezin Y, Danziger M M and Havlin S 2014 Phys. Rev. E 90 012809
[27] Danziger M M, Shekhtman L M, Berezin Y and Havlin S 2016 Europhys. Lett. 115 36002
[28] Chen M, Jin L, Gong X Y, Wang X J and Sun W H 2020 Int. J. Mod. Phys. C 31 2050055
[29] Shekhtmana L M, Danzigerb M M, Vaknin D and Havlin S C R 2018 Comptes Rendus Physique 19 233
[30] Motter A E and Lai Y C 2002 Phys. Rev. E 66 065102
[31] Dou B L and Zhang S Y 2011 J. Syst. Simul. 23 1459
[32] Tan Y G and Wu J 2004 Syst. Eng. Theory Pract. 6 24
[33] Chang G Y, Chang G J and Chen G H 2005 IEEE Trans. Parallel Distrib. Syst. 16 314
[34] Barabsi A L and Albert R 1999 Science 286 509
[35] Li Y, Tang G, Song L J, Xun Z P, Xia H and Hao D P 2013 Acta Phys. Sin. 62 046401 (in Chinese)
[36] Buldyrev S V, Parshani R, Paul G, Stanley H E and Havlin S 2009 Nature 464 1025
[1] Asymmetric image encryption algorithm based ona new three-dimensional improved logistic chaotic map
Guo-Dong Ye(叶国栋), Hui-Shan Wu(吴惠山), Xiao-Ling Huang(黄小玲), and Syh-Yuan Tan. Chin. Phys. B, 2023, 32(3): 030504.
[2] Quantum properties of nonclassical states generated by an optomechanical system with catalytic quantum scissors
Heng-Mei Li(李恒梅), Bao-Hua Yang(杨保华), Hong-Chun Yuan(袁洪春), and Ye-Jun Xu(许业军). Chin. Phys. B, 2023, 32(1): 014202.
[3] Configurational entropy-induced phase transition in spinel LiMn2O4
Wei Hu(胡伟), Wen-Wei Luo(罗文崴), Mu-Sheng Wu(吴木生), Bo Xu(徐波), and Chu-Ying Ouyang(欧阳楚英). Chin. Phys. B, 2022, 31(9): 098202.
[4] Physical aspects of magnetized Jeffrey nanomaterial flow with irreversibility analysis
Fazal Haq, Muhammad Ijaz Khan, Sami Ullah Khan, Khadijah M Abualnaja, and M A El-Shorbagy. Chin. Phys. B, 2022, 31(8): 084703.
[5] Robustness measurement of scale-free networks based on motif entropy
Yun-Yun Yang(杨云云), Biao Feng(冯彪), Liao Zhang(张辽), Shu-Hong Xue(薛舒红), Xin-Lin Xie(谢新林), and Jian-Rong Wang(王建荣). Chin. Phys. B, 2022, 31(8): 080201.
[6] Thermodynamic properties of two-dimensional charged spin-1/2 Fermi gases
Jia-Ying Yang(杨家营), Xu Liu(刘旭), Ji-Hong Qin(秦吉红), and Huai-Ming Guo(郭怀明). Chin. Phys. B, 2022, 31(6): 060504.
[7] Thermodynamic effects of Bardeen black hole surrounded by perfect fluid dark matter under general uncertainty principle
Zhenxiong Nie(聂振雄), Yun Liu(刘芸), Juhua Chen(陈菊华), and Yongjiu Wang(王永久). Chin. Phys. B, 2022, 31(5): 050401.
[8] A quantitative analysis method for contact force of mechanism with a clearance joint based on entropy weight and its application in a six-bar mechanism
Zhen-Nan Chen(陈镇男), Meng-Bo Qian(钱孟波), Fu-Xing Sun(孙福兴), and Jia-Xuan Pan(潘佳煊). Chin. Phys. B, 2022, 31(4): 044501.
[9] Characteristics of vapor based on complex networks in China
Ai-Xia Feng(冯爱霞), Qi-Guang Wang(王启光), Shi-Xuan Zhang(张世轩), Takeshi Enomoto(榎本刚), Zhi-Qiang Gong(龚志强), Ying-Ying Hu(胡莹莹), and Guo-Lin Feng(封国林). Chin. Phys. B, 2022, 31(4): 049201.
[10] Quantum watermarking based on threshold segmentation using quantum informational entropy
Jia Luo(罗佳), Ri-Gui Zhou(周日贵), Wen-Wen Hu(胡文文), YaoChong Li(李尧翀), and Gao-Feng Luo(罗高峰). Chin. Phys. B, 2022, 31(4): 040302.
[11] Comparison of formation and evolution of radiation-induced defects in pure Ni and Ni-Co-Fe medium-entropy alloy
Lin Lang(稂林), Huiqiu Deng(邓辉球), Jiayou Tao(陶家友), Tengfei Yang(杨腾飞), Yeping Lin(林也平), and Wangyu Hu(胡望宇). Chin. Phys. B, 2022, 31(12): 126102.
[12] Information flow between stock markets: A Koopman decomposition approach
Semba Sherehe, Huiyun Wan(万慧云), Changgui Gu(顾长贵), and Huijie Yang(杨会杰). Chin. Phys. B, 2022, 31(1): 018902.
[13] Cascading failures of overload behaviors using a new coupled network model between edges
Yu-Wei Yan(严玉为), Yuan Jiang(蒋沅), Rong-Bin Yu(余荣斌), Song-Qing Yang(杨松青), and Cheng Hong(洪成). Chin. Phys. B, 2022, 31(1): 018901.
[14] Small activation entropy bestows high-stability of nanoconfined D-mannitol
Lin Cao(曹琳), Li-Jian Song(宋丽建), Ya-Ru Cao(曹亚茹), Wei Xu(许巍), Jun-Tao Huo(霍军涛), Yun-Zhuo Lv(吕云卓), and Jun-Qiang Wang(王军强). Chin. Phys. B, 2021, 30(7): 076103.
[15] Influence of temperature and alloying elements on the threshold displacement energies in concentrated Ni-Fe-Cr alloys
Shijun Zhao(赵仕俊). Chin. Phys. B, 2021, 30(5): 056111.
No Suggested Reading articles found!