Complex networks: small-world, scale-free and beyond
By Xiaofan Wang, Guanrong Chen. 2003
1,413 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/2b5af418c3f52240ea20bb3a8edb20b913b37770
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In Computer Networks and Communications
In Statistical and Nonlinear Physics
Citations in the corpus
- Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics (2005, background): “Histograms of the lengths of these connections form approximate power-law distributions, suggesting that the connectivity is fractal [69] with a small number of sites having very high numbers, which are sites of catastrophic loss of function from very small lesions, such as those to the midbrain ret”
- Adaptive synchronization of an uncertain complex dynamical network (2005, result): “Compared with some similar results [3,5,15], our sufficient conditions for net work synchronization are rather broad and the controllers are ve y simple.”
- Pinning a complex dynamical network to its equilibrium (2004, background): “Most of these phenomena cannot be well described by the E–R random graph theory; therefore they stimulate a strong desire of building up new network models ([13]–[ 16 ], and references therein].”
- Novel decentralized adaptive strategies for the synchronization of complex networks (2009, methodology): “In our simulations, we consider a network of N = 1000 nodes connected through a Barabàsi-Albert scale-free network, constructed from N0 = 5 starting nodes (for further details on this topology, see [3], [21], [26]).”
- Sliding-Mode Control With Soft Computing: A Survey (2009, background): “in highly connected networks of individual subsystems [34].”
- A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System (2018, background): “Complex network [9]–[11] theory has undergone an explosive”
- A time-varying complex dynamical network model and its controlled synchronization criteria (2004, positive): “However, the synchronization of the time-invariant dynamical network (4) is completely determined only by its inner-coupling matrix A and the eigenvalues of the coupling configuration matrix C [ 14 ,15].”