Structural Controllability for a Class of Complex Networks With Root Strongly Connected Components

Wu, Xu; Wang, Xinwei; Gu, Mengqi; Jiang, Guoping

doi:10.1109/access.2020.2971616

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…There are intensive studies on the original framework of network controllability [13,14,15,16,17,18,19,20,21,22], with extensions to temporal or multilayer networks [7,23,24,25,26], cost of control [7,27,28,29,30,31,32], and biological and social networks [33,34,35,36,37,38,39,40,41,42,43]. Yet, except for a few studies [44,45,46,47], most of the previous works mainly consider "neutral" networks, whose degree distributions do not have correlations.…”

Section: Introductionmentioning

confidence: 99%

The network asymmetry caused by the degree correlation and its effect on the bimodality in control

Yu,

Liang,

Wang

et al. 2021

Preprint

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Our ability to control a whole network can be achieved via a small set of driver nodes. While the minimum number of driver nodes needed for control is fixed in a given network, there are multiple choices for the driver node set. A quantity used to investigate this multiplicity is the fraction of redundant nodes in the network, referring to nodes that do not need any external control. Previous work has discovered a bimodality feature characterized by a bifurcation diagram: networks with the same statistical property would stay with equal probability to have a large or small fraction of redundant nodes.Here we find that this feature is rooted in the symmetry of the directed network, where both the degree distribution and the degree correlation can play a role. The in-in and out-out degree correlation will suppress the bifurcation, as networks with such degree correlations are asymmetric under network transpose. The out-in and in-out degree correlation do not change the network symmetry, hence the bimodality feature is preserved. However, the out-in degree correlation will change the critical average degree needed for the bifurcation. Hence by fixing the average degree of networks and tuning out-in degree correlation alone, we can observe a similar bifurcation diagram. We conduct analytical analyses that adequately explain the emergence of bimodality caused by out-in degree correlation. We also propose a quantity, taking both degree distribution and degree correlation into consideration, to predict if a network would be at the upper or lower branch of the bifurcation. As is well known that most real networks are not neutral, our results extend our understandings of the controllability of complex networks.

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