Percolation on networks with dependence links

doi:10.1088/1674-1056/23/7/076402

Abstract As a classical model of statistical physics, the percolation theory provides a powerful approach to analyze the network structure and dynamics. Recently, to model the relations among interacting agents beyond the connection of the networked system, the concept of dependence link is proposed to represent the dependence relationship of agents. These studies suggest that the percolation properties of these networks differ greatly from those of the ordinary networks. In particular, unlike the well known continuous transition on the ordinary networks, the percolation transitions on these networks are discontinuous. Moreover, these networks are more fragile for a broader degree distribution, which is opposite to the famous results for the ordinary networks. In this article, we give a summary of the theoretical approaches to study the percolation process on networks with inter- and inner-dependence links, and review the recent advances in this field, focusing on the topology and robustness of such networks.

Keywords: percolation network dependence link phase transition

Received: 11 March 2014
Revised: 17 March 2014
Accepted manuscript online:

PACS:	64.60.ah	(Percolation)
	89.75.Hc	(Networks and genealogical trees)
	64.60.aq	(Networks)
	89.75.Fb	(Structures and organization in complex systems)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11275186 and 91024026).

Corresponding Authors: Wang Bing-Hong
E-mail: bhwang@ustc.edu.cn

About author: 64.60.ah; 89.75.Hc; 64.60.aq; 89.75.Fb

Cite this article:

Li Ming (李明), Wang Bing-Hong (汪秉宏) Percolation on networks with dependence links 2014 Chin. Phys. B 23 076402

[1]	Newman M E J 2010 Networks: An Introduction (New York: Oxford University Press)
[2]	Albert R and Barabási A L 2002 Rev. Mod. Phys. 74 47
[3]	Cohen R and Havlin S 2010 Complex Networks: Structure, Robustness and Function (Cambridge: Cambridge University Press)
[4]	Albert R, Jeong H and Barabási A L 2000 Nature 406 378
[5]	Cohen R, Erez K, Ben-Avraham D and Havlin S 2000 Phys. Rev. Lett. 85 4626
[6]	Callaway D S, Newman M E J, Strogatz S H and Watts D J 2000 Phys. Rev. Lett. 85 5468
[7]	Cohen R, Erez K, Ben-Avraham D and Havlin S 2001 Phys. Rev. Lett. 86 3682
[8]	Parshani R, Buldyrev S V and Havlin S 2011 Proc. Natl. Acad. Sci. USA 108 1007
[9]	Buldyrev S V, Parshani R, Paul G, Stanley H E and Havlin S 2010 Nature 464 1025
[10]	Wiff H 1994 Generatingfunctionology (London: Academic)
[11]	Newman M E J, Strogatz S H and Watts D J 2001 Phys. Rev. E 64 026118
[12]	Newman M E J 2002 Phys. Rev. E 66 016128
[13]	Son S W, Grassberger P and Paczuski M 2011 Phys. Rev. Lett. 107 195702
[14]	Son S W, Bizhani G, Christensen C, Grassberger P and Paczuski M 2012 Europhys. Lett. 97 16006
[15]	Newman M E J 2003 Social Networks 25 83
[16]	Berchenko Y, Artzy-Randrup Y, Teicher M and Stone L 2009 Phys. Rev. Lett. 102 138701
[17]	Baxter G J, Dorogovtsev S N, Goltsev A V and Mendes J F F 2012 Phys. Rev. Lett. 109 248701
[18]	Newman M E J 2003 Phys. Rev. E 67 026126
[19]	Vázquez A and Moreno Y 2003 Phys. Rev. E 67 015101
[20]	Zhou D, Stanley H E, D'Agostino G and Scala A 2012 Phys. Rev. E 86 066103
[21]	Newman M E J 2009 Phys. Rev. Lett. 103 058701
[22]	Huang X, Shao S, Wang H, Buldyrev S V, Stanley H E and Havlin S 2013 Europhys. Lett. 101 18002
[23]	Li W, Bashan A, Buldyrev A V, Stanley H E and Havlin S 2012 Phys. Rev. Lett. 108 228702
[24]	Bashan A, Berezin Y, Buldyrev S V and Havlin S 2013 Nat. Phys. 9 667
[25]	Buldyrev A V, Shere N W and Cwilich G A 2011 Phys. Rev. E 83 016112
[26]	Parshani R, Rozenblat C, Ietri D, Ducruet C and Havlin S 2010 Europhys. Lett. 92 68002
[27]	Hu Y, Zhou D, Zhang R, Han Z, Rozenblat C and Havlin S 2013 Phys. Rev. E 88 052805
[28]	Cellai D, López E, Zhou J, Gleeson J P and Bianconi G 2013 Phys. Rev. E 88 052811
[29]	Li M, Liu R R, Jia C X and Wang B H 2013 New J. Phys. 15 093013
[30]	Parshani R, Buldyrev S V and Havlin S 2010 Phys. Rev. Lett. 105 048701
[31]	Zhou D, Gao J, Stanley H E and Havlin S 2013 Phys. Rev. E 87 052812
[32]	Valdez L D, Macri P A, Stanley H E and Braunstein L A 2013 Phys. Rev. E 88 050803
[33]	Shao J, Buldyrev S V, Havlin S and Stanley H E 2011 Phys. Rev. E 83 036116
[34]	Bashan A, Parshani R and Havlin S 2011 Phys. Rev. E 83 051127
[35]	Bashan A and Havlin S 2011 J. Stat. Phys. 145 686
[36]	Huang X, Gao J, Buldyrev S V, Havlin S and Stanley H E 2011 Phys. Rev. E 83 065101
[37]	Dong G, Gao J, Tian L, Du R and He Y 2012 Phys. Rev. E 85 016112
[38]	Zhou D, Gao J, Stanley H E and Havlin S 2013 Phys. Rev. E 87 052812
[39]	Gao J, Buldyrev S V, Havlin S and Stanley H E 2011 Phys. Rev. Lett. 107 195701
[40]	Gao J, Buldyrev S V, Havlin S and Stanley H E 2012 Nat. Phys. 8 40
[41]	Gao J, Buldyrev S V, Stanley H E, Xu X and Havlin S 2013 Phys. Rev. E 88 062816
[42]	Brummitt C D, Lee K M and Goh K I 2012 Phys. Rev. E 85 045102
[43]	Nicosia V, Bianconi G, Latora V and Barthelemy M 2013 Phys. Rev. Lett. 111 058701
[44]	Kim J Y and Goh K I 2013 Phys. Rev. Lett. 111 058702
[45]	De Domenico M, Solé-Ribalta A, Cozzo E, Kivelä M, Moreno Y, Porter M A, Gómez S and Arenas A 2013 Phys. Rev. X 3 041022
[46]	Bianconi G 2013 Phys. Rev. E 87 062806
[47]	Halu A, Mukherjee S and Bianconi G 2014 Phys. Rev. E 89 012806
[48]	Watanabe S and Kabashima Y 2014 Phys. Rev. E 89 012808
[49]	D'Agostino G and Scala A 2014 Networks of Networks: The Last Frontier of Complexity (Berlin: Springer International Publishing)
[50]	Leicht E A and D'Souza R M 2009 arXiv:0907.0894
[51]	Newman M E J 2012 Nat. Phys. 8 25
[52]	Hu Y, Ksherim B, Cohen R and Havlin S 2011 Phys. Rev. E 84 066116
[53]	Li M, Liu R R, Jia C X and Wang B H 2014 (unpublished)
[54]	Saumell-Mendiola A, Serrano M Á and Boguuñá M 2012 Phys. Rev. E 86 026106
[55]	Wang H, Li Q, D'Agostino G, Havlin S, Stanley H E and Van Mieghem P 2013 Phys. Rev. E 88 022801
[56]	Gómez S, Díaz-Guilera A, Gómez-Gardeñes J, Pérez-Vicente C J, Moreno Y and Arenas A 2013 Phys. Rev. Lett. 110 028701
[57]	Gómez-Gardeñes J, Reinares I, Arenas A and Floria L M 2013 Sci. Rep. 2 00620
[58]	Zhen W, Attila S and Perc M 2013 Sci. Rep. 3 01183

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