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This parallelization significantly speeds up the whole algorithm and allows designing medium sized networks as well as vast networks relatively fast.
We show that, for small and medium sized networks, the number of messages required for successful detection is actually smaller than the minimal number computed theoretically.
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Using Cytoscape [ 35], we identified a few major hubs (nodes with many connections) from the medium sized network (~12K) using tissue-wide meta-analysis.
The authors also realize that this method is good for small and medium size networks of up to 100 nodes.
This paper shows by proof of concept that a Collaboration Layer can be modelled and designed to handle the collaborative communication, which enables robots in small to medium size networks to solve tasks collaboratively.
Realistic simulation of aggregates of neurons often utilises compartmental models which limit the scope of the simulations with single processor architectures to small or medium size networks (typically hundreds of neurons).
Interestingly, our experience show that this is often the case for medium size networks.
In the following, we present an algorithm which combines the advantages of both of these classes, fast inference of medium size networks that can quantitatively model the dynamic behavior of the inferred network.
Second, the solution obtained in the first stage is improved through an iterative optimisation procedure employing a model that we have developed previously and was proven efficient in detecting communities in small to medium size networks through a global maximum of the modularity metric (OptMod, [ 30]).
A case study of a medium size network is carried out using an agent-based simulator.
The experiments are presented using two networks, a small network and a medium size network, simulating the city of Seville.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com