3,471 research outputs found
On the pinning strategy of complex networks
In pinning control of complex networks, a tacit believing is that the system
dynamics will be better controlled by pinning the large-degree nodes than the
small-degree ones. Here, by changing the number of pinned nodes, we find that,
when a significant fraction of the network nodes are pinned, pinning the
small-degree nodes could generally have a higher performance than pinning the
large-degree nodes. We demonstrate this interesting phenomenon on a variety of
complex networks, and analyze the underlying mechanisms by the model of star
networks. By changing the network properties, we also find that, comparing to
densely connected homogeneous networks, the advantage of the small-degree
pinning strategy is more distinct in sparsely connected heterogenous networks
Effects of the network structural properties on its controllability
In a recent paper, it has been suggested that the controllability of a
diffusively coupled complex network, subject to localized feedback loops at
some of its vertices, can be assessed by means of a Master Stability Function
approach, where the network controllability is defined in terms of the spectral
properties of an appropriate Laplacian matrix. Following that approach, a
comparison study is reported here among different network topologies in terms
of their controllability. The effects of heterogeneity in the degree
distribution, as well as of degree correlation and community structure, are
discussed.Comment: Also available online at: http://link.aip.org/link/?CHA/17/03310
Pinning Complex Networks by a Single Controller
In this paper, without assuming symmetry, irreducibility, or linearity of the
couplings, we prove that a single controller can pin a coupled complex network
to a homogenous solution. Sufficient conditions are presented to guarantee the
convergence of the pinning process locally and globally. An effective approach
to adapt the coupling strength is proposed. Several numerical simulations are
given to verify our theoretical analysis
Controllability of Social Networks and the Strategic Use of Random Information
This work is aimed at studying realistic social control strategies for social
networks based on the introduction of random information into the state of
selected driver agents. Deliberately exposing selected agents to random
information is a technique already experimented in recommender systems or
search engines, and represents one of the few options for influencing the
behavior of a social context that could be accepted as ethical, could be fully
disclosed to members, and does not involve the use of force or of deception.
Our research is based on a model of knowledge diffusion applied to a
time-varying adaptive network, and considers two well-known strategies for
influencing social contexts. One is the selection of few influencers for
manipulating their actions in order to drive the whole network to a certain
behavior; the other, instead, drives the network behavior acting on the state
of a large subset of ordinary, scarcely influencing users. The two approaches
have been studied in terms of network and diffusion effects. The network effect
is analyzed through the changes induced on network average degree and
clustering coefficient, while the diffusion effect is based on two ad-hoc
metrics defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. The results, obtained through
simulations on synthetic networks, show a rich dynamics and strong effects on
the communication structure and on the distribution of knowledge and skills,
supporting our hypothesis that the strategic use of random information could
represent a realistic approach to social network controllability, and that with
both strategies, in principle, the control effect could be remarkable
Local pinning of networks of multi-agent systems with transmission and pinning delays
We study the stability of networks of multi-agent systems with local pinning
strategies and two types of time delays, namely the transmission delay in the
network and the pinning delay of the controllers. Sufficient conditions for
stability are derived under specific scenarios by computing or estimating the
dominant eigenvalue of the characteristic equation. In addition, controlling
the network by pinning a single node is studied. Moreover, perturbation methods
are employed to derive conditions in the limit of small and large pinning
strengths.Numerical algorithms are proposed to verify stability, and simulation
examples are presented to confirm the efficiency of analytic results.Comment: 6 pages, 3 figure
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