Visibility graph approach to extreme event series

doi:10.1088/1674-1056/acd62b

中国物理B ›› 2023, Vol. 32 ›› Issue (10): 100505-100505.doi: 10.1088/1674-1056/acd62b

Visibility graph approach to extreme event series

Jing Zhang(张晶)^1,2, Xiaolu Chen(陈晓露)¹, Haiying Wang(王海英)¹, Changgui Gu(顾长贵)¹, and Huijie Yang(杨会杰)^1,†

1 Department of Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, China;
2 Department of Business, Wuxi Taihu University, Wuxi 214064, China

收稿日期:2023-03-25 修回日期:2023-05-12 接受日期:2023-05-17 出版日期:2023-09-21 发布日期:2023-10-08
通讯作者: Huijie Yang E-mail:hjyang@ustc.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 11805128, 11875042, and 11505114) and the Shanghai Project for Construction of Top Disciplines, China (Grant No. USST-SYSBIO).

Visibility graph approach to extreme event series

Jing Zhang(张晶)^1,2, Xiaolu Chen(陈晓露)¹, Haiying Wang(王海英)¹, Changgui Gu(顾长贵)¹, and Huijie Yang(杨会杰)^1,†

1 Department of Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, China;
2 Department of Business, Wuxi Taihu University, Wuxi 214064, China

Received:2023-03-25 Revised:2023-05-12 Accepted:2023-05-17 Online:2023-09-21 Published:2023-10-08
Contact: Huijie Yang E-mail:hjyang@ustc.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 11805128, 11875042, and 11505114) and the Shanghai Project for Construction of Top Disciplines, China (Grant No. USST-SYSBIO).

摘要/Abstract

摘要： An extreme event may lead to serious disaster to a complex system. In an extreme event series there exist generally non-trivial patterns covering different time scales. Investigations on extreme events are currently based upon statistics, where the patterns are merged into averages. In this paper from extreme event series we constructed extreme value series and extreme interval series. And the visibility graph is then adopted to display the patterns formed by the increases/decreases of extreme value or interval faster/slower than the linear ones. For the fractional Brownian motions, the properties for the constructed networks are the persistence, threshold, and event-type-independent, $e.g.$, the degree distributions decay exponentially with almost identical speeds, the nodes cluster into modular structures with large and similar modularity degrees, and each specific network has a perfect hierarchical structure. For the volatilities of four stock markets (NSDQ, SZI, FTSE100, and HSI), the properties for the former three's networks are threshold- and market-independent. Comparing with the factional Brownian motions, their degree distributions decay exponentially but with slower speeds, their modularity behaviors are significant but with smaller modularity degrees. The fourth market behaves similar qualitatively but different quantitatively with the three markets. Interestingly, all the transition frequency networks share an identical backbone composed of nine edges and the linked graphlets. The universal behaviors give us a framework to describe extreme events from the viewpoint of network.

关键词: extreme events, visibility graph

Abstract: An extreme event may lead to serious disaster to a complex system. In an extreme event series there exist generally non-trivial patterns covering different time scales. Investigations on extreme events are currently based upon statistics, where the patterns are merged into averages. In this paper from extreme event series we constructed extreme value series and extreme interval series. And the visibility graph is then adopted to display the patterns formed by the increases/decreases of extreme value or interval faster/slower than the linear ones. For the fractional Brownian motions, the properties for the constructed networks are the persistence, threshold, and event-type-independent, $e.g.$, the degree distributions decay exponentially with almost identical speeds, the nodes cluster into modular structures with large and similar modularity degrees, and each specific network has a perfect hierarchical structure. For the volatilities of four stock markets (NSDQ, SZI, FTSE100, and HSI), the properties for the former three's networks are threshold- and market-independent. Comparing with the factional Brownian motions, their degree distributions decay exponentially but with slower speeds, their modularity behaviors are significant but with smaller modularity degrees. The fourth market behaves similar qualitatively but different quantitatively with the three markets. Interestingly, all the transition frequency networks share an identical backbone composed of nine edges and the linked graphlets. The universal behaviors give us a framework to describe extreme events from the viewpoint of network.

Key words: extreme events, visibility graph

中图分类号: (Time series analysis)

05.45.Tp

89.65.Gh (Economics; econophysics, financial markets, business and management)

Jing Zhang(张晶), Xiaolu Chen(陈晓露), Haiying Wang(王海英), Changgui Gu(顾长贵), and Huijie Yang(杨会杰). Visibility graph approach to extreme event series[J]. 中国物理B, 2023, 32(10): 100505-100505.

Jing Zhang(张晶), Xiaolu Chen(陈晓露), Haiying Wang(王海英), Changgui Gu(顾长贵), and Huijie Yang(杨会杰). Visibility graph approach to extreme event series[J]. Chin. Phys. B, 2023, 32(10): 100505-100505.

参考文献

[1] Chowdhurya S N, Ray A, Dana S K and Ghosh D 2022 Phys. Rep. 966 1
[2] Majumdar S N, Pal A and Schehr F 2020 Phys. Rep. 840 1
[3] Scheffer M, Bascompte J, Brock W A, Brovkin V, Carpenter S R, et al. 2009 Nature 461 53
[4] Battiston S, Farmer J D, Flache A, Garlaschelli D, Haldane A G, Heesterbee K H, et al. 2016 Science 351 818
[5] Arani B M S, Carpenter S R, Lahti L, Nes E H V and Scheffer M 2021 Science 372 eaay4895
[6] Bunde A, Eichner J F, Kantelhardt J W and Havlin S 2005 Phys. Rev. Lett. 94 048701
[7] Bunde A, Eichner J F, Kantelhardt J W and Havlin S 2006 Phys. Rev. E 73 016130
[8] Santhanam M S and Kantz H 2008 Phys. Rev. E 78 051113
[9] Nicolis C and Nicolis G 2009 Phys. Rev. E 80 061119
[10] Aghamohammadi C and Crutchfield J P 2017 Phys. Rev. E 95 032101
[11] Gao Z K, Small M and Kurths J 2016 Europhys. Lett. 116 50001
[12] Zou Y, Donner R V, Marwan N, Donges J F and Kurths J 2019 Phys. Rep. 787 1
[13] Zhang J and Small M 2006 Phys. Rev. Lett. 96 238701
[14] Zhang J, Luo X, Nakamura T, Sun J and Small M 2007 Phys. Rev. E 75 016218
[15] Zhang J, Sun J, Luo X, Zhang K, Nakamura T and Small M 2008 Physica D 237 2856
[16] Yang Y and Yang H J 2008 Physica A 387 1381
[17] Marwan M, Donges J F, Zou Y, Donner R V and Kurths J 2009 Phys. Lett. A 373 4246
[18] Donner R, Zou Y, Donges J, Marwan N and Kurths J 2010 New J. Phys. 12 033025
[19] Gao Z and Jin N D 2009 Phys. Rev. E 79 066303
[20] Pham T D 2017 Europhys. Lett. 118 20003
[21] Tumminello M, Aste T, Matteo T D and Mantegna R N 2005 Proc. Natl. Acad. Sci. USA 102 10421
[22] Xu X, Zhang and Small M 2008 Proc. Natl. Acad. Sci. USA 105 19601
[23] Lacasa L, Luque B, Ballesteros F, Luque J and Nuno C 2008 Proc. Natl. Acad. Sci. USA 105 4972
[24] Luque B, Lacasa L, Ballesteros F and Luque J 2009 Phys. Rev. E 80 046103
[25] Ni X H, Jiang Z Q and Zhou W X 2009 Phys. Lett. A 373 3822
[26] Xiao Q, Pan X, Li X L, Mutua S, Yang H J, Jiang Y, Wang J Y and Zhang Q J 2014 Chin. Phys. B 23 078904
[27] Mantegna R N and Stanley H E 2000 Introduction to Econophysics: Correlations Complexity in Finance (Cambridge University Press, Cambridge)
[28] https://uk.finance.yahoo.com/world-indices/ Accessed in July 20, 2022
[29] Albert R and Barabasi A L 2002 Rev. Mod. Phys. 74 47
[30] Lacasa L, Luque L, Luque B and Nuno J C 2009 Europhys. Lett. 86 30001
[31] Girvan M and Newman M E J 2002 Proc. Natl. Acad. Sci. USA 99 7821
[32] Newman M E J and Girvan M 2004 Phys. Rev. E 69 026113
[33] Ravasz E and Barabasi A L 2003 Phys. Rev. E 67 026112
[34] McCullough M, Small M, Stemler T and Iu H C 2015 Chaos 25 053101
[35] Stephen M, Gu C G and Yang H J 2015 PLoS ONE 10 e0143015
[36] Iacovacci J and Lacasa L 2016 Phys. Rev. E 93 042309
[37] Mutua S, Gu C G and Yang H J 2016 Chaos 26 053107
[38] Kulp C W, Chobot J M, Freitas H R and Sprechini G D 2016 Chaos 26 073114
[39] McCullough M, Small M, Iu H H C and Stemler T 2017 Phil. Trans. Roy. Soc. A 375 20170016
[40] McCullough M, Sakellariou K, Stemler T and Small M 2017 Chaos 27 035814
[41] Zhang J Y, Zhou J, Tang M, Guo H, Small M and Zou Y 2017 Sci. Rep. 7 7795
[42] Guo H, Zhang J Y, Zou Y and Guan S G 2018 Front. Phys. 13 130508
[43] Ren H G, Yuan Q S, Semba S, Weng T F, Gu C G and Yang H J 2020 Phys. Lett. A 384 126781
[44] Wang Y, Weng T F, Deng S G, Gu C G and Yang H J 2019 Chaos 29 023109

Visibility graph approach to extreme event series

Visibility graph approach to extreme event series

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

Metrics

本文评价

推荐阅读 0

[1]	Huang-Jing Ni(倪黄晶), Ruo-Yu Du(杜若瑜), Lei Liang(梁磊), Ling-Ling Hua(花玲玲), Li-Hua Zhu(朱丽华), and Jiao-Long Qin(秦姣龙). Two-dimensional horizontal visibility graph analysis of human brain aging on gray matter[J]. 中国物理B, 2023, 32(7): 78501-078501.
[2]	Lu Ma(马璐), Yan-lin Ren(任彦霖), Ai-jun He(何爱军), De-qiang Cheng(程德强), and Xiao-dong Yang(杨小冬). Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph[J]. 中国物理B, 2023, 32(11): 110506-110506.
[3]	封泰晨, 张珂铨, 苏海晶, 王晓娟, 龚志强, 张文煜. Spatiotemporal distribution characteristics and attribution of extreme regional low temperature event[J]. 中国物理B, 2015, 24(10): 109201-109201.
[4]	肖琴, 潘雪, 李信利, Mutua Stephen, 杨会杰, 蒋艳, 王建勇, 张庆军. Row-column visibility graph approach to two-dimensional landscapes[J]. , 2014, 23(7): 78904-078904.
[5]	樊超, 郭进利 . Visitor flow pattern of Expo 2010[J]. 中国物理B, 2012, 21(7): 70209-070209.
[6]	章大全, 封国林, 胡经国. Trend of extreme precipitation events over China in last 40 years[J]. 中国物理B, 2008, 17(2): 736-742.