Abstract
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.
Highlights
Complex networks have received growing interest from various disciplines to model and study the network topology and interaction between nodes within a modeled network [1,2,3]
We propose a novel network reconstruction method based on the neural networks (NNs) technique
We evaluate the performance the proposed method on simulations in two classical complex networks small-world network and scale-free based on simulations in two classical complex networks (1) small-world network and (2) scale-free networks,generated generatedby byWatts
Summary
Complex networks have received growing interest from various disciplines to model and study the network topology and interaction between nodes within a modeled network [1,2,3]. Many approaches have been proposed to optimize conventional networks against random failure and intentional attack compared to the conventional complex networks [7,8,9,10]. These approaches have been mainly concentrated on designing network topology based on various optimization techniques to minimize the damage to the network. On these we chose we the and (2) link information of M undamaged networks are Based available Basedassumptions, on these assumptions, multiple-layer perceptron neural network (MLPNN), which is one of the frequently used.
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