Journal
PHYSICAL REVIEW E
Volume 95, Issue 4, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.95.042318
Keywords
-
Categories
Funding
- NSF [PHY-1505000, CMMI 1125290]
- NIH [5R01AG021133]
- DOD [HDTRA1-14-1-0017]
- ONR [N00014-14-1-0738]
- DOE [DE-AC07-051D14517]
- Army Research Laboratory Cooperative Agreement (ARL Network Science CTA) [W911NF-09-2-0053]
- European Commission [FP7317532, 641191]
- National Natural Science Foundation of China [61320106005, 91530320, 61374160]
- Innovation Scientists and Technicians Troop Construction Projects of Henan Province [154200510012]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1125290] Funding Source: National Science Foundation
Ask authors/readers for more resources
When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdos-Renyi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p = p(m). We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree < k > and that it is determined by p(m) < k >. In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available