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The analysis of large-scale Boolean network dynamics is of great importance in understanding complex phenomena where systems are characterized by a large number of components. The computational cost to reveal the number of attractors and the period of each attractor increases exponentially as the number of nodes in the networks increases. This paper presents an efficient algorithm to find attractors...
The problem of approximation to large-scale Boolean networks is considered. First, we assume a large-scale Boolean network is aggregated into several sub-networks. Using the outputs(or inputs) of each sub-network as new state variables, a new simplified time-varying network is obtained. Then a time-invariant Boolean network is used to approximate each subsystem. Observed data are used to find the...
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