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

TL;DR

This paper investigates how degree correlation and network asymmetry influence the bimodality in network controllability, revealing how certain correlations suppress or preserve this feature and proposing a predictive measure.

Contribution

It uncovers the role of degree correlation in network asymmetry affecting bimodality in control, providing analytical explanations and a new predictive quantity.

Findings

Out-in degree correlation preserves bimodality.

Out-in degree correlation alters the critical average degree for bifurcation.

Degree correlation can be tuned to observe bifurcation behavior.

Abstract

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.