52 research outputs found
Control Centrality and Hierarchical Structure in Complex Networks
We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks
Controllability of a swarm of topologically interacting autonomous agents
Controllability of complex networks has been the focal point of many recent
studies in the field of complexity. These landmark advances shed a new light on
the dynamics of natural and technological complex systems. Here, we analyze the
controllability of a swarm of autonomous self-propelled agents having a
topological neighborhood of interactions, applying the analytical tools
developed for the study of the controllability of arbitrary complex directed
networks. To this aim we thoroughly investigate the structural properties of
the swarm signaling network which is the information transfer channel
underpinning the dynamics of agents in the physical space. Our results show
that with 6 or 7 topological neighbors, every agent not only affects, but is
also affected by all other agents within the group. More importantly, still
with 6 or 7 topological neighbors, each agent is capable of full control over
all other agents. This finding is yet another argument justifying the
particular value of the number of topological neighbors observed in field
observations with flocks of starlings.Comment: 9 pages, 3 figures. arXiv admin note: text overlap with
arXiv:1401.259
Controlling complex policy problems: a multimethodological approach using system dynamics and network controllability
Notwithstanding the usefulness of system dynamics in analyzing complex policy
problems, policy design is far from straightforward and in many instances
trial-and-error driven. To address this challenge, we propose to combine system
dynamics with network controllability, an emerging field in network science, to
facilitate the detection of effective leverage points in system dynamics models
and thus to support the design of influential policies. We illustrate our
approach by analyzing a classic system dynamics model: the World Dynamics
model. We show that it is enough to control only 53% of the variables to steer
the entire system to an arbitrary final state. We further rank all variables
according to their importance in controlling the system and we validate our
approach by showing that high ranked variables have a significantly larger
impact on the system behavior compared to low ranked variables
Control efficacy of complex networks
Acknowledgements W.-X.W. was supported by CNNSF under Grant No. 61573064, and No. 61074116 the Fundamental Research Funds for the Central Universities and Beijing Nova Programme, China. Y.-C.L. was supported by ARO under Grant W911NF-14-1-0504.Peer reviewedPublisher PD
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