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At present, the tracing technique has been applied to trace power flow [18, 19, 20], as well as in the unreliability of general complex networks, generation systems, and BPS [21, 22, 23, 24].
Geographical threshold models were recently proven to have good results in scale-free networks generation [7].
In this section, we consider related works that encouraged us to create a new model for complex networks generation.
In our work, we suggest a new model for scale-free networks generation, which is based on the matrix factorization and has a geographical interpretation.
The algorithm combines methods to exploit mechanisms underpinning scale free networks generation, i.e. network growth and preferential attachment (PA), with a technique to optimally reduce the effects of measurement noise in the data on the reliability of the inference results, i.e. the Constrained Total Least Squares (CTLS) algorithm.
The IPA networks generation algorithm transformed the gene list into a network set using Global Molecular Network connections and Ingenuity Pathways Knowledge Base.
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The proposed approach uses the evolutionary Artificial Neural Network Generation and Training (ANNGaT) algorithm to generate the neural network system.
Here, to facilitate comparisons, the same gene selection criterion (inputs to network generation) and correlation measure were applied to generate the weighted and unweighted networks in each dataset.
The Sydney Morning Herald compared him to the outlaw Ned Kelly Henry Porterr likened him to the English radical John Wilkes; and for virtual reality pioneer Mark Pesce he is the Che Guevara of the network generation.
Data acquisition and network generation.
Network generation, visualization, modeling and simulation.
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