Identification of key recovering node for spatial networks

doi:10.1088/1674-1056/acb75f

中国物理B ›› 2023, Vol. 32 ›› Issue (6): 68901-068901.doi: 10.1088/1674-1056/acb75f

Identification of key recovering node for spatial networks

Zijian Yan(严子健)¹, Yongxiang Xia(夏永祥)^1,†, Lijun Guo(郭丽君)², Lingzhe Zhu(祝令哲)¹, Yuanyuan Liang(梁圆圆)¹, and Haicheng Tu(涂海程)¹

1 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China;
2 China Intelligent Transportation Systems Association, Beijing 100160, China

收稿日期:2022-10-12 修回日期:2023-01-19 接受日期:2023-01-31 出版日期:2023-05-17 发布日期:2023-05-24
通讯作者: Yongxiang Xia E-mail:xiayx@hdu.edu.cn
基金资助:
Project supported by Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ23F030012), and the Fundamental Research Funds for the Provincial Universities of Zhejiang (Grant No. GK229909299001-018).

Identification of key recovering node for spatial networks

Zijian Yan(严子健)¹, Yongxiang Xia(夏永祥)^1,†, Lijun Guo(郭丽君)², Lingzhe Zhu(祝令哲)¹, Yuanyuan Liang(梁圆圆)¹, and Haicheng Tu(涂海程)¹

1 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China;
2 China Intelligent Transportation Systems Association, Beijing 100160, China

Received:2022-10-12 Revised:2023-01-19 Accepted:2023-01-31 Online:2023-05-17 Published:2023-05-24
Contact: Yongxiang Xia E-mail:xiayx@hdu.edu.cn
Supported by:
Project supported by Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ23F030012), and the Fundamental Research Funds for the Provincial Universities of Zhejiang (Grant No. GK229909299001-018).

摘要/Abstract

摘要： Many networks in the real world have spatial attributes, such as location of nodes and length of edges, called spatial networks. When these networks are subject to some random or deliberate attacks, some nodes in the network fail, which causes a decline in the network performance. In order to make the network run normally, some of the failed nodes must be recovered. In the case of limited recovery resources, an effective key node identification method can find the key recovering node in the failed nodes, by which the network performance can be recovered most of the failed nodes. We propose two key recovering node identification methods for spatial networks, which are the Euclidean-distance recovery method and the route-length recovery method. Simulations on homogeneous and heterogeneous spatial networks show that the proposed methods can significantly recover the network performance.

关键词: complex networks, spatial networks, congestion, key recovering node

Abstract: Many networks in the real world have spatial attributes, such as location of nodes and length of edges, called spatial networks. When these networks are subject to some random or deliberate attacks, some nodes in the network fail, which causes a decline in the network performance. In order to make the network run normally, some of the failed nodes must be recovered. In the case of limited recovery resources, an effective key node identification method can find the key recovering node in the failed nodes, by which the network performance can be recovered most of the failed nodes. We propose two key recovering node identification methods for spatial networks, which are the Euclidean-distance recovery method and the route-length recovery method. Simulations on homogeneous and heterogeneous spatial networks show that the proposed methods can significantly recover the network performance.

Key words: complex networks, spatial networks, congestion, key recovering node

中图分类号: (Complex systems)

89.75.-k

89.75.Fb (Structures and organization in complex systems) 89.75.Hc (Networks and genealogical trees) 89.40.-a (Transportation)

Zijian Yan(严子健), Yongxiang Xia(夏永祥), Lijun Guo(郭丽君), Lingzhe Zhu(祝令哲), Yuanyuan Liang(梁圆圆), and Haicheng Tu(涂海程). Identification of key recovering node for spatial networks[J]. 中国物理B, 2023, 32(6): 68901-068901.

参考文献

[1] Feng X, He S W and Li Y B2019 Transportmetrica A: Transport Sci. 15 1825
[2] Pagani G A and Aiello M2013 Physica A 392 2688
[3] Gan C Q, Yang X F, Liu W P, Zhu Q Y, Jin J and He L2014 Commun. Nonlinear Sci. Numer. Simul. 19 2785
[4] Freeman L C1978 Social Networks 1 215
[5] Ganesh A, Massoulié L and Towsley D2005 IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, March 13-17, 2005, Miami, USA, p. 1455
[6] Lin J Y and Ban Y F2013 Transport Rev. 33 658
[7] Boss M, Elsinger H, Summer M and Thurner 4 S2004 Quant. Finance 4 677
[8] Fang P J, Zhang D M and He M H2015 Chin. Phys. Lett. 32 88901
[9] Dong G G, Wang F, Shekhtman L M, Danziger M M, Fan J F, Du R J, Liu J G, Tian L X, Stanley H E and Havlin S2021 Proc. Natl. Acad. Sci. USA 118 e1922831118
[10] Xia Y X, Wang C, Shen H L and Song H N2020 Physica A 559 125071
[11] Liang Y Y, Xia Y X and Yang X H2022 Physica A 591 126800
[12] Tu H C, Xia Y X, Iu H H and Chen X2018 IEEE Trans. Circuits Syst. II: Express Briefs 66 126
[13] Wu J, Liu Y and Ruan Q2022 Nonlinear Analysis: Hybrid Systems 46 101216
[14] Han W T, Yi P and Ma H L2019 Acta Phys. Sin. 68 186401 (in Chinese)
[15] Li S B, Wu J J, Gao Z Y, Lin Y and Fu B B2011 Acta Phys. Sin. 60 050701 (in Chinese)
[16] Cowen L, Ideker T, Raphael B and Sharan R2017 Nat. Rev. Genet. 18 551
[17] Yang P, Li X L, Wu M, Kwoh C K and Ng S K2011 PloS One 6 e21502
[18] Liu Y, Sanhedrai H, Dong G, Shekhtman L M, Wang F, Buldyrev S V and Havlin S2021 Natl. Sci. Rev. 8 nwaa229
[19] Righi A W, Saurin T A and Wachs P2015 Reliability Engineering and System Safety 141 142
[20] Liu T, Bai G, Tao J, Zhang Y A, Fang Y and Xu B2022 Reliability Engineering and System Safety 226 108663
[21] Liu J G, Ren Z M, Guo Q and Wang B H2013 Acta Phys. Sin. 62 178901 (in Chinese)
[22] Goh K, Oh E, Kahng B and Kim D2003 Phys. Rev. E 67 017101
[23] Brandes U, Borgatti S P and Freeman L C2016 Social Networks 44 153
[24] Liu J X, Chen S D and Wang Y G2012 International Conference on Machine Learning and Cybernetics, July 15-17, 2012, Xi'an, China, p. 920
[25] Tian H, Nie B and Zhang W J2010 International Conference on Machine Vision and Human-machine Interface, April 24-25, 2010, Kaifeng, China, p. 737
[26] Lin D W2018 J. Algorithms Comput. Technol. 12 62
[27] Yang Y Z, Yu L, Wang X, Zhou Z L, Chen Y and Kou T2019 Physica A 526 121118
[28] Fan W L, Liu Z G and Hu P2013 Phys. Scr. 88 065201
[29] Shan X W, Zhang X and Tse C K2022 IEEE Trans. Circuits Syst. II: Express Briefs 69 4859
[30] Yang Y Z, Hu M and Huang T Y2020 Chin. Phys. B 29 088903
[31] Yan X L, Cui Y P and Ni S J2020 Chin. Phys. B 29 048902
[32] Dall J and Christensen M2002 Phys. Rev. E 66 016121
[33] Jiang L R, Jin X Y, Xia Y X, Ouyang B, Wu D P and Chen X2014 Int. J. Distrib. Sensor Networks 10 764698
[34] Lin H, Xia Y X and Jiang L R2022 Acta Phys. Sin. 71 068901 (in Chinese)
[35] Motter A E and Lai Y C2002 Phys. Rev. E 66 065102
[36] Liu J, Xiong Q Y, Shi W R, Shi X and Wang K2016 Physica A 452 209
[37] Burt R S2004 Am. J. Sociol. 110 349
[38] Yu H, Cao X, Liu Z and Li Y J2017 Physica A 486 318

Identification of key recovering node for spatial networks

Identification of key recovering node for spatial networks

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