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Callaway (2000)
85Phys Rev Lett
A. Bashan, Roni Parshani, S. Havlin (2011)
Percolation in networks composed of connectivity and dependency linksPhysical review. E, Statistical, nonlinear, and soft matter physics, 83 5 Pt 1
Changjun Gao, Y. Gong, Xin Wang, Xuelei Chen (2010)
Cosmological models with Lagrange multiplier fieldPhysics Letters B, 702
D. Callaway, M. Newman, M. Newman, S. Strogatz, D. Watts, D. Watts (2000)
Network robustness and fragility: percolation on random graphs.Physical review letters, 85 25
Buldyrev (2010)
464Nature
(2009)
Percolation on interacting networks, Preprint at http://arxiv.org/abs/0907
Bashan (2011)
83Phys Rev
M. Newman (2000)
The structure of scientific collaboration networks.Proceedings of the National Academy of Sciences of the United States of America, 98 2
Jianxi Gao, S. Buldyrev, H. Stanley, S. Havlin (2011)
Networks formed from interdependent networksNature Physics, 8
Buldyrev (2011)
016112Phys Rev
Chaoming Song, S. Havlin, H. Makse (2005)
Self-similarity of complex networksNature, 433
M. Newman, M. Newman, S. Strogatz, D. Watts, D. Watts (2000)
Random graphs with arbitrary degree distributions and their applications.Physical review. E, Statistical, nonlinear, and soft matter physics, 64 2 Pt 2
J. Stelling, Steffen Klamt, K. Bettenbrock, S. Schuster, E. Gilles (2002)
Metabolic network structure determines key aspects of functionality and regulationNature, 420
Fu (2013)
392Physica
T. Kalisky, R. Cohen (2005)
Width of percolation transition in complex networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 73 3 Pt 2
S. Valverde, R. Solé, M. Bedau, N. Packard (2006)
Topology and evolution of technology innovation networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 76 5 Pt 2
Newman (2001)
98Proc Natl Acad Sci
Jia Shao, S. Buldyrev, S. Havlin, H. Stanley (2010)
Cascade of failures in coupled network systems with multiple support-dependent relationsPhysical review. E, Statistical, nonlinear, and soft matter physics, 83 3 Pt 2
Kalisky (2006)
035101Phys Rev
Song (2005)
433Nature
Newman (2002)
016128Phys Rev
Newman (2001)
026118Phys Rev
Shao (2011)
036116Phys Rev
Stelling (2002)
420Nature
(2013)
Physica A 392
Nagler (2011)
7Nat Phys
Albert (1999)
401Nature
S. Havlin, N. Araújo, S. Buldyrev, C. Dias, Roni Parshani, G. Paul, H. Stanley (2009)
Catastrophic cascade of failures in interdependent networksNature, 464
Huang (2011)
065101Phys Rev
Liu (2012)
109Phys Rev Lett
Gao (2012)
8Nat Phys
Tao Fu, Yini Chen, Zhen Qin, Liping Guo (2013)
Percolation on shopping and cashback electronic commerce networksPhysica A-statistical Mechanics and Its Applications, 392
Jianxi Gao, S. Buldyrev, S. Havlin, H. Stanley (2010)
Robustness of a Network of NetworksPhysical review letters, 107 19
M. Newman, M. Newman (2002)
Spread of epidemic disease on networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 66 1 Pt 2
Yang-Yu Liu, E. Csóka, Haijun Zhou, M. Pósfai (2012)
Core percolation on complex networksPhysical review letters, 109 20
R. Albert, Hawoong Jeong, A. Barabási (1999)
Internet: Diameter of the World-Wide WebNature, 401
S. Buldyrev, Nathaniel Shere, G. Cwilich (2010)
Interdependent networks with identical degrees of mutually dependent nodes.Physical review. E, Statistical, nonlinear, and soft matter physics, 83 1 Pt 2
(1999)
Nature 401
S. Steingrube, M. Timme, F. Wörgötter, P. Manoonpong (2010)
Self-organized adaptation of a simple neural circuit enables complex robot behaviourArXiv, abs/1105.1386
Valverde (2007)
056118Phys Rev
Xuqing Huang, Jianxi Gao, S. Buldyrev, S. Havlin, H. Stanley (2010)
Robustness of interdependent networks under targeted attackPhysical review. E, Statistical, nonlinear, and soft matter physics, 83 6 Pt 2
Parshani (2010)
105Phys Rev Lett
Roni Parshani, S. Buldyrev, S. Havlin (2010)
Interdependent networks: reducing the coupling strength leads to a change from a first to second order percolation transition.Physical review letters, 105 4
Abstract Realistic network-like systems are usually composed of multiple networks with interacting relations such as school-enterprise research and development (R&D) collaboration networks. Here, we study the percolation properties of a special class of R&D collaboration network, namely institute-enterprise R&D collaboration networks (IERDCNs). We introduce two actual IERDCNs to show their structural properties, and we present a mathematical framework based on generating functions for analyzing an interacting network with any connection probability. Then,we illustrate the percolation threshold and structural parameter arithmetic in the sub-critical and supercritical regimes.We compare the predictions of our mathematical framework and arithmetic to data for two real R&D collaboration networks and a number of simulations. We find that our predictions are in remarkable agreement with the data. We show applications of the framework to electronics R&D collaboration networks
Open Physics – de Gruyter
Published: Jan 1, 2015
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