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Decision-Oriented Rough Set Methods Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Controllability in Directed Complex Networks: Granular Computing Perspective

Authors : Yunyun Yang, Gang Xie, Zehua Chen

Published in: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Publisher: Springer International Publishing

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Abstract

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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previous chapter Connectedness of Graph and Matroid by Covering-Based Rough Sets
next chapter Dynamic Maintenance of Rough Fuzzy Approximations with the Variation of Objects and Attributes
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Metadata
Title
Controllability in Directed Complex Networks: Granular Computing Perspective
Authors
Yunyun Yang
Gang Xie
Zehua Chen
Copyright Year
2015
Book
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Print ISBN: 978-3-319-25782-2

Electronic ISBN: 978-3-319-25783-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-25783-9_15

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