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Open AccessArticle

Reconstructing Damaged Complex Networks Based on Neural Networks

by Ye Hoon Lee 1 and Insoo Sohn 2,*
1
Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
2
Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Symmetry 2017, 9(12), 310; https://doi.org/10.3390/sym9120310
Received: 2 October 2017 / Revised: 22 November 2017 / Accepted: 9 December 2017 / Published: 9 December 2017
(This article belongs to the Special Issue Graph Theory)
Despite recent progress in the study of complex systems, reconstruction of damaged networks due to random and targeted attack has not been addressed before. In this paper, we formulate the network reconstruction problem as an identification of network structure based on much reduced link information. Furthermore, a novel method based on multilayer perceptron neural network is proposed as a solution to the problem of network reconstruction. Based on simulation results, it was demonstrated that the proposed scheme achieves very high reconstruction accuracy in small-world network model and a robust performance in scale-free network model. View Full-Text
Keywords: network reconstruction; neural networks; small world networks; scale free networks network reconstruction; neural networks; small world networks; scale free networks
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Lee, Y.H.; Sohn, I. Reconstructing Damaged Complex Networks Based on Neural Networks. Symmetry 2017, 9, 310.

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