NETWORK SCIENCE FACES THE CHALLENGE AND OPPORTUNITY: EXPLORING “NETWORK OF NETWORKS” AND ITS UNIFIED THEORETICAL FRAMEWORK

Jin-Qing Fang; Quan-Hui Liu; Ming Tang; Qiang Liu; Yong Li

doi:10.11948/2016002

2016 Volume 6 Issue 1

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Jin-Qing Fang, Quan-Hui Liu, Ming Tang, Qiang Liu, Yong Li. NETWORK SCIENCE FACES THE CHALLENGE AND OPPORTUNITY: EXPLORING “NETWORK OF NETWORKS” AND ITS UNIFIED THEORETICAL FRAMEWORK[J]. Journal of Applied Analysis & Computation, 2016, 6(1): 12-29. doi: 10.11948/2016002

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Jin-Qing Fang, Quan-Hui Liu, Ming Tang, Qiang Liu, Yong Li. NETWORK SCIENCE FACES THE CHALLENGE AND OPPORTUNITY: EXPLORING “NETWORK OF NETWORKS” AND ITS UNIFIED THEORETICAL FRAMEWORK[J]. Journal of Applied Analysis & Computation, 2016, 6(1): 12-29. doi: 10.11948/2016002

NETWORK SCIENCE FACES THE CHALLENGE AND OPPORTUNITY: EXPLORING “NETWORK OF NETWORKS” AND ITS UNIFIED THEORETICAL FRAMEWORK

1 China institute of atomic energy, p. o. box 275-68, Beijing 102413, China;
2 Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China

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Abstract

Abstract

In the era of big data, network science is facing new challenges and opportunities. This review article focuses on discussing one of the hottest subjects of network science-"network of networks" (NON). The main features, several typical examples and the main progress for NON are outlined, including the epidemic spreading in multilayer coupled networks. Finally the most challenging tasks for NON are proposed.
- Network of networks /
- challenges and opportunities /
- multilayer coupled networks /
- spreading dynamics
MSC: 05C82;70E55;37F99

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References

[1]	R. Albert and A. L. Barabási, Statistical mechanics of complex networks, Rev. Mod. Phys., 74(2002), 47-97. Google Scholar
[2]	N. T. J. Bailey, The Mathematical Theory of Infectious Diseases and its Applications, Hafner Press, New York, (1975). Google Scholar
[3]	A. Banerjee, A. G. Chandrasekhar, E. Duflo, and M. O. Jackson, The diffusion of microfinance, Science, 341(2013)(6144). Google Scholar
[4]	A. L. Barabási, The network takeover, Nature Physics, 8(2011)(1), 14-16. Google Scholar
[5]	A. L. Barabási, Universality in network dynamics, Nature Physics, 9(2013), 673-681. Google Scholar
[6]	A. L. Barabási, Bursts:The Hidden Patterns Behind Everything We Do, from Your E-mail to Bloody Crusades, Plume Books, USA, 2011. Google Scholar
[7]	A. L. Barabási and R. Albert, Emergence of scaling in random networks, Science, 286(1999)(5439), 509-512. Google Scholar
[8]	A. L. Barabási and R. Albert, Statistical mechanics of complex networks, Rev Mod Phys, 74(2002)(1), 47-97. Google Scholar
[9]	A. L. Barabási and J. Frangos Linked:the new science of networks science of networks, Perseus Books Group, USA, 2002. Google Scholar
[10]	D. S. Bassett and E. Bullmore, Small-World Brain Networks, Neuroscientist, 12(2006)(6), 512-23. Google Scholar
[11]	Q. Bi and J.Q. Fang, Network science and Statistical Physics, Bejing University Press, Beijing, 2011. Google Scholar
[12]	Q. Bi, J. Q. Fang and J. Liu, Subdynamics:Peculia Branch of Statistical Physics Theory, Journal of University of Shanghais Science and Technology, 34(2012)(2), 11-137. Google Scholar
[13]	Q. Bi, Z. T. Hu and J. Q. Fang, A Framework for non-equilibrium and equilibrium statistical ensemblem, Complex systems and complexity science, 4(2010)(4), 39-51. Google Scholar
[14]	S. Boccaleti, G. Bianconi and R. Criado, et al, Structure and dynamics of multilayer networks, Physics Reports, 544(2014)(1), 1-122. Google Scholar
[15]	C. D. Brummitt, K. M. Lee and K. I. Goh, Multiplexity-facilitated cascades in networks, Physical Review E, 85(2012)(4), 045102. Google Scholar
[16]	C. Buono, L. G. Alvarez-Zuzek, P. A. Macri and L. A. Braunstein, Epidemics in partially overlapped multiplex networks, PloS one, 9(2014)(3), e92200. Google Scholar
[17]	D. Centola, An experimental study of homophily in the adoption of health behavior, Science, 334(2011), 1269-1272. Google Scholar
[18]	G. R. Chen, Network sciencere search:Some recent progress in China and beyond, National Science Review, 1(2014)(345). Google Scholar
[19]	P. Cui, M. Tang and Z. X. Wu, Message spreading in networks with stickiness and persistence:Large clustering does not always facilitate large-scale diffusion, Scientific reports, 4(2014)(6303). Google Scholar
[20]	A. X. Cui, W. Wang, M. Tang, Y. Fu, X. Liang and Y. Do, Efficient allocation of heterogeneous response times in information spreading process, Chaos, 24(2014)(3), 033113. Google Scholar
[21]	M. Dickison, S. Havlin and H. E. Stanley, Epidemics on interconnected networks, Phys. Rev. E, 85(2012), 066109. Google Scholar
[22]	S. N. Dorogovtsev and J. F. F. Mendes, Evolution of networks, Adv Phys., 51(2002)(4), 1079-1187. Google Scholar
[23]	V. M. Eguiluz, et al, Scale-Free Brain Functional Networks, Phys. Rev. Lett., 94(2005), 018102. Google Scholar
[24]	J. Q. Fang, Progress and Challenges in China's Network Science:Network Science Forum in 2014 the Tenth Anniversary, Complex System and Complexity Science, 12(2014)(2), 1-8. Google Scholar
[25]	J. Q. Fang, Review and outlook:Congratulations on the 10th anniversary of the national complex network meeting, Invited to report, 201410th China National Complex Network Conference, Changsha:October 17-19, 2014. Google Scholar
[26]	J. Q. Fang, Exploring progress on brain network (I), Chinese Journal of Nature, 6(2012), 344-349. Google Scholar
[27]	J. Q. Fang, Exploring progress on brain network (Ⅱ), Chinese Journal of Nature, 35(2013)(2),135-143. Google Scholar
[28]	J. Q. Fang, Big data wave impact network science and engineering challenges and opportunities, Chinese Journal of Nature, 5(2013)(13). Google Scholar
[29]	J. Q. Fang, Steering Halo-Chaos and Exploring Network Science (in Chinese), Beijing China Atomic Energy Press, Beijing, 2008. Google Scholar
[30]	J. Q. Fang, The network science and the brain, Neural neural informatics and computing, Zhejiang science and technology press, Hangzhou, 2012. Google Scholar
[31]	J. Q. Fang, Network complexity pyramid with five levels, Int J Systems, Control and Communications, 1(2009)(4), 453-477. Google Scholar
[32]	J. Q. Fang, Q. Bi, Y. Li, et al, A Harmonious Unifying Hybrid Preferential Model and its Universal Properties for Complex Dynamical Networks, Science in China Series G:Physics, Mechanics and Astronomy, 50(2007)(3), 379-396. Google Scholar
[33]	J. Q. Fang, Q. Bi, Y. Li, et al, Toward a Harmonious Unifying Hybrid Model for Any Evolving Complex Networks, Advances in Complex Systems, 10(2007)(2), 117-141. Google Scholar
[34]	J. Q. Fang, Q. Bi, Y. Li, et al, Sensitivity of exponents of three power laws to hybrid ratios in weighted HUHPM, Chin. Phys. Lett., 24(2007)(1), 279-282. Google Scholar
[35]	J. Q. Fang, Q. Bi, Y. Li, et al, Small world effects on a harmonious unifying preferential model network, Commun. Theor. Phys., 48(2007)(2), 377-383. Google Scholar
[36]	J. Q. Fang and Y. Li, Advences in Unified Hybrid Theoretical Model of Network Science (In Chinese), Advances in Mechanics, 38(2008)(6), 663-678. Google Scholar
[37]	J. Q. Fang and Y. Li, Transition Features from Simplicity-universality to Complexity-diversification under the UHNM-VSG, Commun. Theor. Phys., 53(2010)(2), 389-398. Google Scholar
[38]	J. Q. Fang, Y. Li and Q. Bi, From a Harmonious Uunifying Hybrid Model Toward A Large Unifying Hybrid Network Model, International Journal of Modern Physics B, 21(2007)(30), 5121-5132. Google Scholar
[39]	J. Q. Fang, Y. Li and Q. Liu, Three types of network complexity pyramid, Advances in Network Complexity, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. (2013), DOI:10.1002/9783527670468.ch04. Google Scholar
[40]	J. Q. Fang, Y. Li, Q. Liu, et al, Try to talk about several features and Thinking for High-Technology Networks, Complex Networks Theory and Applications, Shanghai System Science Press, Shanghai, 2008. Google Scholar
[41]	J.Q. Fang, Q. Bi, Y. Li, X. B. Lu and Q. Liu, Advances in theoretical models of network science, Frontiers of Physics, 2(2007)(1), 109-124. Google Scholar
[42]	J. Q. Fang and M Tang, Network Science Faces the Challenge and Opportunity:Exploring "network of networks" and its unified theoretical framework, Invited to report, 201511th China Forum on Network Science, Shanghai:April 17-19, 2015. Google Scholar
[43]	J. Q. Fang, X. F. Wang and Z. G. Zheng, Dynamical complexity of nonlinear networks (In Chinese), Progress in Physics, 29(2009)(1), 1-74. Google Scholar
[44]	J. Q. Fang, X. F. Wang, Z. G. Zheng, et al, New Interdisciplinary science:Network Science, Progress in physics (in Chinese), (I) 27(2007)(3), 239-448:(Ⅱ) 27(2007)(4), 361-448. Google Scholar
[45]	S. Funk, E. Gilad, C. Watkins and V. A. A. Jansen, The spread of awareness and its impact on epidemic outbreaks, Proc. Natl. Acad. Sci. USA, 106(2009), 6872. Google Scholar
[46]	S. Funk, E. Gilad and V. A. A. Jansen, Endemic disease, awareness, and local behavioural response, J. Theor. Biol., 264(2010)(2), 501-509. Google Scholar
[47]	S. Funk, M. Salathé and V. A. A. Jansen, Modelling the influence of human behaviour on the spread of infectious diseases:a review, J. R. Soc. Interface, 7(2010), 1247-1256. Google Scholar
[48]	J.X. GAO, S. V. Buldyrev, H. E. Stanley and S. Havlin, Network formed from interdependent networks, Nature Phys., 8(2012)(1), 40-48. Google Scholar
[49]	J. X. Gao, D. Q. Li and S. Havlin, From a single network to a network of networks, National Science Review, 1(2014), 346-356. Google Scholar
[50]	R. J. Garten, C. T. Davis, C. A. Russell C A, et al, Antigenic and genetic characteristics of swine-origin 2009 A (H1N1) influenza viruses circulating in humans, Science, 325(2009)(5937), 197-201. Google Scholar
[51]	S. Gómez, A. Diaz-Guilera, J. Gomez-Gardeñes and C. J, Moreno and A. Arenas.Diffusion dynamics on multiplex networks, Physical review letters, 110(2013)(2), 028701. Google Scholar
[52]	K. Gong, M. Tang, P. M. Hui, H. F. Zhang, D. Younghae and Y. C. Lai, An efficient immunization strategy for community networks, PLoS ONE, 8(2013)(12), e83489. Google Scholar
[53]	M. Granovetter, The strength of weak ties, Am. J. Sociol, 78(1973)(1360). Google Scholar
[54]	C. Granell, S. Gómez and A. Arenas, Dynamical interplay between awareness and epidemic spreading in multiplex networks, Phy. Rev. Lett., 111(2013), 128701. Google Scholar
[55]	C. Granell, S. Gómez and A. Arenas, Competing spreading processes on multiplex networks:Awareness and epidemics, Phys. Rev. E, 90(2014), 012808. Google Scholar
[56]	Q. Guo, X. Jiang, Y. Lei, et al, Two-stage effects of awareness cascade on epidemic spreading in multiplex networks, Physical Review E, 91(2015)(1), 012822. Google Scholar
[57]	J. L. Guo and X. Y. Zhu, Emergence of Scaling in Hypernetworks, Acta Phys. Sin., 63(2014)(9), 090207. Google Scholar
[58]	F. Hu, X. X. Zhao and X. J. Ma, A supernetwork evolution model building and characteristic analysis, Chinese science, physics, mechanics, astronomy, lancet, 1(2013), 16-22. Google Scholar
[59]	F. Hu, H. X. Zhao, J. B. He et al, An evolving model for hyper graph-structurebased scientific collaboration networks, Acta Phys. Sin., 62(2013)(19), 198901. Google Scholar
[60]	X. F. Hu, X. Y. He and D. H. Rao, A Methodology for investigation the capabilities of command and coordination for system of systems operation based on complex network theory, Complex Systems and Complexity Science, 12(2015)(2), 9-17. Google Scholar
[61]	W. L. Jeffries, The number of recent sex partners among bisexual men in the United States, Perspectives on sexual and reproductive health, 43(2011)(3), 151-157. Google Scholar
[62]	T. G. Lewis, Network Science:Theory And applications, Wiley, 2009, USA. Google Scholar
[63]	K. M. Lee, C. D. Brummitt and K. I. Goh, Threshold cascades with response heterogeneity in multiplex networks, Physical Review E, 90(2014)(6), 062816. Google Scholar
[64]	Y. Li, J. Q Fang and Q. Liu, Briefly Review of China High Technology Networks, Complex Sciences Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 5(2009), 1238-1247. Google Scholar
[65]	M. Li and B. H. wang, The structure and robustness of mulitilayer networks, Complex Systems and Complexity Science, 12(2015)(2), 32-37. Google Scholar
[66]	F. Liljeros, C. R. Edling and L. A. N. Amaral, Sexual networks:implications for the transmission of sexually transmitted infections, Microbes and infection, 5(2)(2003), 189-196. Google Scholar
[67]	Q. Liu, J. Q. Fang and Y. Li, A unified dynamic scaling property for the UHNTF, Frontiers of Physics, 9(2014)(2), 240-245. Google Scholar
[68]	Q. Liu, J. Q. Fang and Y. Li, Three-layered supernetwork evolution model and application for China-World's Top 500 enterprises supernetwork, Intern. J. Modern Phys. C, 25(2014)(4), 1440002. Google Scholar
[69]	Q. Liu, J. Q. Fang and Y. Li, Some Characteristics of Three-Layer Supernetwork Evolution Model, Complex Systems and Complexity Science, 12(2015)(2), 64-71. Google Scholar
[70]	Y. Y. Liu, J. J. Slotine and A. L. Barabási, Controllability of complex networks, Nature, 473(2011)(7346), 167-173. Google Scholar
[71]	E. Massaro and F. Bagnoli, Epidemic spreading and risk perception in multiplex networks:a self-organized percolation method, Physical Review E, 90(2014)(5), 052817. Google Scholar
[72]	B. Min, K. I. Goh, Layer-crossing overhead and information spreading in multiplex social networks, arXiv:1307.2967, 2013. Google Scholar
[73]	M. E. J. Newman, Networks:an introduction, Oxford University Press, 2010. Google Scholar
[74]	M. E. J. Newman, The structure of scientific collaboration networks, Proc Natl Acad Sc USA, 98(2001)(2), 404-409. Google Scholar
[75]	M. E. J. Newman, A. L. Barabási and D. J. Watts, The structure and dynamics of networks, Princeton University Press, 2006. Google Scholar
[76]	Z. Ping and Z. T. Wang, Supernetwork Theory and its application, Beijing:Science Press, Beijing, 2008. Google Scholar
[77]	Z. Ruan, M. Tang and Z. Liu, Epidemic spreading with information-driven vaccination, Phys. Rev. E, 86(2012), 036117. Google Scholar
[78]	Z. Ruan, M. Tang and Liu Z, How the contagion at links influences epidemic spreading, The European Physical Journal B, 86(2013)(4), 1-6. Google Scholar
[79]	F. D. Sahneh, F. N. Chowdhury and C. M. Scoglio, On the existence of a threshold for preventive behavioral responses to suppress epidemic spreading, Sci. Rep., 2(2012)(632). Google Scholar
[80]	A. Saumell-Mendiola, M. Á. Serrano and M. Boguñá, Epidemic spreading on interconnected networks, Phys. Rev. E, 86(2012), 026106. Google Scholar
[81]	D. H. Shi, The network degree distribution theory, Chinese Higher education press editorial, Beijing, 2011. Google Scholar
[82]	P. Shu, M. Tang, K. Gong and Y. Liu, Effects of weak ties on epidemic predictability on community networks, Chaos:An Interdisciplinary Journal of Nonlinear Science, 22(2012)(4), 043124. Google Scholar
[83]	P. Shu, W. Wang, M. Tang, et al, Numerical identification of epidemic thresholds for susceptible-infected-recovered model on finite-size networks, Chaos, 25(2015)(6), 063104. Google Scholar
[84]	M. Tang, L. Liu and Z. Liu, Influence of dynamical condensation on epidemic spreading in scale-free networks, Physical Review E, 79(2009)(1), 016108. Google Scholar
[85]	M. Tang, Z. Liu and B. Li, Epidemic spreading by objective traveling. EPL (Europhysics Letters), 87(2009)(1), 18005. Google Scholar
[86]	M. Tang, Z. Liu and J. Zhou, Condensation in a zero range process on weighted scale-free networks, Physical Review E, 74(2006)(3), 036101. Google Scholar
[87]	M. Tang, T. Zhou, Efficient routing strategies in scale-free networks with limited bandwidth, Physical review E, 84(2011)(2), 026116. Google Scholar
[88]	H. Wang, Q. Li, G. D'Agostino, S. Havlin and H. E. Stanley, Effect of the interconnected network structure on the epidemic threshold, Physical Review E, 88(2013)(2), 022801. Google Scholar
[89]	W. Wang, P. Shu, Y. X. Zhu M. Tang and Y. C. Zhang, Dynamics of social contagions with limited contact capacity, Chaos, 25(2015), 103102. Google Scholar
[90]	B. Wang, G. Tanaka, H. Suzuki and K. Aihara, Epidemic spread on interconnected metapopulation networks, Physical Review E, 90(2014)(3), 032806. Google Scholar
[91]	W. Wang. M. Tang, H. Yang, et al, Asymmetrically interacting spreading dynamics on complex layered networks, Sci. Rep., 4(2014)(5097). Google Scholar
[92]	D. J. Watts, A simple model of global cascades on random networks, Proc. Natl. Acad. Sci. USA, 99, 5766(2002). Google Scholar
[93]	Q. Wu, X. Fu, M. Small and X. J. Xu, The impact of awareness on epidemic spreading in networks, Chaos, 22(2012), 013101. Google Scholar
[94]	E. H. W. Xu, W. Wang, C. Xu, M. Tang, Y. Do and P. M. Hui, Suppressed epidemics in multirelational networks, Phys. Rev. E, 92(2015), 022812. Google Scholar
[95]	O. Yağan and V. Gligor, Analysis of complex contagions in random multiplex networks, PHYSICAL REVIEW E, 86(2012), 036103. Google Scholar
[96]	H. Yang, M. Tang and H. F. Zhang, Efficient community-based control strategies in adaptive networks, New Journal of Physics, 14(2012)(12), 123017. Google Scholar
[97]	H. Yang, M. Tang and T. Gross, Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes, Sci. Rep., 5(2015)(13122). Google Scholar
[98]	H. P. Young, The dynamics of social innovation, Proc. Natl Acad. Sci. USA, 108(2011), 21285-21291. Google Scholar
[99]	W. Wang, M. Tang, H. F. Zhang, H. Gao, Y. Do and Z. H. Liu, Epidemic spreading on complex networks with general degree and weight distributions, Physical Review E, 90(2014)(4), 042803. Google Scholar
[100]	Z. T. Wang, Z. Ping, Super network study, Journal of management, 5(2008)(1), 1-16. Google Scholar
[101]	D. J. Watts and S. H. Strogatz, Collective dynamics of small-world networks, Nature, 393(1998)(6684), 440-442. Google Scholar
[102]	J. W. Wang, L. L. Rong, Q. H. Deng, et al, Evolving hypernetwork model, Eur. Phys. J. B, 77(2010), 493-498. Google Scholar
[103]	H. F. Zhang, J. R. Xie, M. Tang, et al, Suppression of epidemic spreading in complex networks by local information based behavioral responses, Chaos:An Interdisciplinary Journal of Nonlinear Science, 24(2014)(4), 043106. Google Scholar
[104]	D. Zhao, L. Wang, S. Li, Z. Wang, L. Wanga and B. Gao, Immunization of Epidemics in Multiplex Networks, PLoS one, 9(2014)(11), e112018. Google Scholar
[105]	H. F. Zhang, P. P. Shu, M. Tang, et al, Preferential imitation of vaccinating behavior can invalidate the targeted subsidy on complex network, arXiv, 1503.08048(2015). Google Scholar
[106]	H. F. Zhang, Z. X. Wu, M. Tang and Y. C. Lai, Effects of behavioral response and vaccination policy on epidemic spreading-an approach based on evolutionary-game dynamics, Sci. Rep., 4(2014)(5666). Google Scholar
[107]	Y. X. Zhu, X. G. Zhang, G. Q. Sun, M. Tang, T. Zhou and Z. K. Zhang, Influence of reciprocal links in social networks, PLoS ONE, 9(2014)(7), e103007. Google Scholar
[108]	C. S. Zhou, et al, Hierarchical Organization Unveiled by Functional Connectivity in Complex Brain Networks, Phys. Rev. Lett., 97(2006)(23), 238103. Google Scholar
[109]	Z. K. Zhang and C. Liu, A hypergraph model of social tagging networks, J. Stat. Mech, 2010, P10005. Google Scholar
[110]	V. Zlatic, G. Ghoshal and G. Caldarelli, Hypergraph topological quantities for tagged social networks, Phys. Rev. E, 80(2009), 036118. Google Scholar
[111]	X. Zhang, Multilayer networks:Concepts, theories and data, Complex Systems and Complexity Science, 12(2015)(2), 103-107. Google Scholar