Abstract
We explore the recently introduced Structured Nodes (SN) network model, for which earlier work has shown its capability in matching several topological properties of complex networks. We consider a diverse set of empirical biological complex networks as targets and we use an evolutionary algorithm (EA) approach to identify input for the SN model allowing it to generate networks similar to these targets.
We find that by using the EA the structural fit between SN networks and the targets is improved.
The combined SN/EA approach is a promising direction to further investigate the growth, properties and behaviour of biological networks.
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Govan, G., Chlanda, J., Corne, D., Xenos, A., Frisco, P. (2013). Finding Biologically Plausible Complex Network Topologies with a New Evolutionary Approach for Network Generation. In: Emmerich, M., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. Advances in Intelligent Systems and Computing, vol 227. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01128-8_5
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DOI: https://doi.org/10.1007/978-3-319-01128-8_5
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