Intelligent regulation of university faculty interdisciplinary collaboration networks based on complex network topology evolution and stochastic differential equations. [PDF]
Sci RepRen S.
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Stability and chaos analysis of neurological disorder of complex network with fractional order comparative study. [PDF]
Sci RepFarman M +4 more
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Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data. [PDF]
Sci RepCiba M +5 more
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A complex network perspective on spatiotemporal evolution of extreme precipitation over the middle and lower reaches of the Yangtze river. [PDF]
Sci RepHu Z, Feng A, Gu C, Zhao P, Wang Q.
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Fitting soil extracellular enzyme activity into the complex network of abiotic and biotic soil properties often associated with soil health. [PDF]
Front MicrobiolTaggart MG +9 more
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Synchronization on Complex Networks of Networks
IEEE Transactions on Neural Networks and Learning Systems, 2014In this paper, pinning synchronization on complex networks of networks is investigated, where there are many subnetworks with the interactions among them. The subnetworks and their connections can be regarded as the nodes and interactions of the networks, respectively, which form the networks of networks.
Renquan Lu +3 more
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The Internet, social networks, power grids, gene regulatory networks, neuronal systems, food webs, social systems, and networks emanating from augmented and virtual reality platforms are all examples of complex networks. Collection and analysis of data from these networks is essential for their understanding.
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Large populations of globally coupled or uncoupled oscillators have been recently shown to exhibit an intriguing echo behavior [Ott, Platig, Antonsen, and Girvan, Chaos: An Interdiscip. J. Nonlinear Sci. 18, 037115 (2008)CHAOEH1054-150010.1063/1.2973816; Chen, Tinsley, Ott, and Showalter, Phys. Rev. X 6, 041054 (2016)2160-330810.1103/PhysRevX.6.041054],
Richa, Phogat +2 more
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Network complexity and generalization
Proceedings of International Conference on Neural Networks (ICNN'97), 2002This paper explains the relationship between complexity of the neural network with sigmoidal hidden neurons and its generalization capability in function approximation. Network complexity is decided in terms of the number of degrees of freedom and their dynamic range.
Sangbong Park, Cheol Hoon Park
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