Controllability in Directed Complex Networks: Granular Computing Perspective
Controlling complex networks to a desired state has been a widespread sense in contemporary science. Usually, we seek a maximum matching of complex networks by matching theory and control those unmatched nodes to achieve the purpose of controlling complex networks. However, for complex networks with high dimensions, it is hard to find its maximum matching or there are copious unmatched nodes that need to be controlled. Therefore, controlling complex networks is extremely strenuous. Motivated by the idea of granular computing (GrC), we take a fine graining preprocessing to the whole complex networks and obtain several different granules. Then find the maximum matching in every granule and control those unmatched nodes to procure the goal of controlling the entire network. At last, the related key problems in GrC-based controllability of complex networks processing framework are discussed.
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