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20 August 2020 Efficient network randomization using multiple edge swapping
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Abstract
The brain connectome can be modeled as a large-scale complex network characterized by high clustering and short network paths. Most studies assess these properties by comparing them to a null network model, generated by randomly rewiring the edges between the nodes of the original network, known as edge swapping. However, this method is computationally expensive and time consuming, mainly in networks with a high number of connections. In this study, we developed an alternative method to create null network models, the allin method. We show that both methods compute null networks with comparable topology, however, the allin method performed the randomization procedure in noticeably less time. The allin method is particularly more effective in the case of high-resolution networks and relatively higher densities. As such, these results suggest that the allin method is a more time efficient alternative to compute null model networks compared to the traditionally used edgeswap method.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mite Mijalkov and Joana B. Pereira "Efficient network randomization using multiple edge swapping", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691T (20 August 2020); https://doi.org/10.1117/12.2569944
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