2,679 research outputs found
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
On the reachability and observability of path and cycle graphs
In this paper we investigate the reachability and observability properties of
a network system, running a Laplacian based average consensus algorithm, when
the communication graph is a path or a cycle. More in detail, we provide
necessary and sufficient conditions, based on simple algebraic rules from
number theory, to characterize all and only the nodes from which the network
system is reachable (respectively observable). Interesting immediate
corollaries of our results are: (i) a path graph is reachable (observable) from
any single node if and only if the number of nodes of the graph is a power of
two, , and (ii) a cycle is reachable (observable) from
any pair of nodes if and only if is a prime number. For any set of control
(observation) nodes, we provide a closed form expression for the (unreachable)
unobservable eigenvalues and for the eigenvectors of the (unreachable)
unobservable subsystem
Structure Identifiability of an NDS with LFT Parametrized Subsystems
Requirements on subsystems have been made clear in this paper for a linear
time invariant (LTI) networked dynamic system (NDS), under which subsystem
interconnections can be estimated from external output measurements. In this
NDS, subsystems may have distinctive dynamics, and subsystem interconnections
are arbitrary. It is assumed that system matrices of each subsystem depend on
its (pseudo) first principle parameters (FPPs) through a linear fractional
transformation (LFT). It has been proven that if in each subsystem, the
transfer function matrix (TFM) from its internal inputs to its external outputs
is of full normal column rank (FNCR), while the TFM from its external inputs to
its internal outputs is of full normal row rank (FNRR), then the NDS is
structurally identifiable. Moreover, under some particular situations like
there are no direct information transmission from an internal input to an
internal output in each subsystem, a necessary and sufficient condition is
established for NDS structure identifiability. A matrix valued polynomial (MVP)
rank based equivalent condition is further derived, which depends affinely on
subsystem (pseudo) FPPs and can be independently verified for each subsystem.
From this condition, some necessary conditions are obtained for both subsystem
dynamics and its (pseudo) FPPs, using the Kronecker canonical form (KCF) of a
matrix pencil.Comment: 16 page
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