Abstract Identifying important nodes and edges in complex networks has always been a popular research topic in network science and also has important implications for the protection of real-world complex systems. Finding the critical structures in a system allows us to protect the system from attacks or failures with minimal cost. To date, the problem of identifying critical nodes in networks has been widely studied by many scholars, and the theory is becoming increasingly mature. However, there is relatively little research related to edges. In fact, critical edges play an important role in maintaining the basic functions of the network and keeping the integrity of the structure. Sometimes protecting critical edges is less costly and more flexible in operation than just focusing on nodes. Considering the integrity of the network topology and the propagation dynamics on it, this paper proposes a centrality measure based on the number of high-order structural overlaps in the first and second-order neighborhoods of edges. The effectiveness of the metric is verified by the infection-susceptibility (SI) model, the robustness index R, and the number of connected branches θ. A comparison is made with three currently popular edge importance metrics from two synthetic and four real networks. The simulation results show that the method outperforms existing methods in identifying critical edges that have a significant impact on both network connectivity and propagation dynamics. At the same time, the near-linear time complexity can be applied to large-scale networks.
Received: 01 October 2022
Revised: 16 November 2022
Accepted manuscript online: 29 November 2022
PACS:
89.75.Fb
(Structures and organization in complex systems)
Corresponding Authors:
Jia-Hui Song
E-mail: songjiahuizz123456@163.com
Cite this article:
Jia-Hui Song(宋家辉) Important edge identification in complex networks based on local and global features 2023 Chin. Phys. B 32 098901
[1] Bonacich P 1972 J. Math. Sociol.2 113 [2] Lü L Y, Zhou T and Zhang Q M 2016 Nat. Comm.7 10168 [3] Kitsak M, Gallos L K and Havlin S 2010 Nat. Phys.6 888 [4] Crescenzi P, D'angelo G and Severini L 2016 ACM. T. Know. Discov. D11 1 [5] Freeman L C 1977 Sociometry40 35 [6] Ma Y, Cao Z and Qi X 2019 Physica A527 121130 [7] Li Z, Ren T and Ma X 2019 Sci. Rep.9 8387 [8] Li Z, Ren T and Xu Y 2020 IEEE Access8 66068 [9] Holme P, Kim B and Yoon C 2002 Stat. Non.65 056109 [10] Xia Y and Hill D J 2008 IEEE Trans. Biomed. Circ. S55 65 [11] Cuadra L, Salcedo-Sanz S and Del Ser J 2015 Energies8 9211 [12] Goltsev A V, Dorogovtsev S N and Oliveira J G 2012 Phys. Rev. Lett.109 128702 [13] Ball M O, Golden B L and Vohra R V 1989 Oper. Res. Lett.8 73 [14] Girvan M, Newman M, Girvan M and Newman M E J 2002 P. Natl. A. Sci.99 7821 [15] Yu E Y, Chen D B and Zhao J Y 2018 Sci. Rep.8 14469 [16] Kanwar K, Kumar H and Kaushal S 2019 Soc. Netw. Anal. Min.12 49 [17] Holme P, Kim B and Yoon C 2002 Phys. Rev. E65 056109 [18] Onnela J P, Saramäki J and Hyvönen J 2007 Nat. Aca. Sci.104 7332 [19] Cheng X Q, Ren F X and Shen H W 2010 J. Stat. Mech-theory. E2010 P10011 [20] Liu Y, Tang M and Zhou T 2015 Sci. Rep.5 131725 [21] Matamalas J T, Arenas A and Gómez S 2018 Sci. Adv.4 eaau4212 [22] Ouyang B, Xia Y and Wang C 2018 IEEE Trans. Biomed. Circ. S65 1244 [23] Xu Y, Ren T and Sun S 2021 Math.9 2531 [24] Yu E Y, Chen D B and Zhao J Y 2018 Sci. Rep.8 14469 [25] Zhao N, Li J and Wang J 2018 Physica A548 123877 [26] Bröhl T and Lehnertz K 2019 Chaos29 1098 [27] Kossinets G and Watts D J 2006 Science311 88 [28] Battiston F 2021 Nat. Phys.17 1093 [29] Rubinov M and Sporns O 2010 Neuroimage52 1059 [30] Reijneveld J C, Ponten S C and Berendse H W 2007 Clin. Neurophysiol.118 2317 [31] Wu T, Zhang X and Liu Z 2022 Front. Phys.17 31504 [32] Wu J, Tse C K and Lau F C M 2013 IEEE Trans. Biomed. Circ.S60 3303 [33] Chen Z, Wu J and Xia Y 2018 IEEE Trans. Circ. I65 115 [34] De la Cruz Cabrera O, Jin J and Noschese S 2022 Appl. Numer. Math.172 186 [35] Cai S M, Hong L and Zhong Q 2011 Complex Syst. Complexity Sci.10 1099 [36] Milanović J V and Zhu W 2017 IEEE T. Smart. Grid9 4637 [37] Yang F, Zhu J and Sun J 2019 J. Netw. Comput. Appl.39 72 [38] Muldoon, Sarah and Feldt 2018 Phys. Life. Rev.24 143 [39] Rubinov M and Sporns O 2010 Neuroimage52 1059 [40] Papo D, Buld'u J M and Boccaletti S 2014 Biol. Sci.369 20130520
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.
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
