Controllability of complex networks

By Yang-Yu Liu, Jean-Jacques E. Slotine, Albert-László Barabási. Nature, 2011

3,140 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3b62c331be63a928278282418de40f63ba9b0e48

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In Molecular Biology

In Statistical and Nonlinear Physics

Citations in the corpus

Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, background): “A main result was that the number of driver nodes required for full control is determined by the network’s degree distribution [469].”
Synchronization in complex networks and its application - A survey of recent advances and challenges (2014, methodology): “Therefore, how to exactly characterize basin stability by using tools from the nonlinear system theory (Khalil, 2002) poses a challenging and important topic.”
The fundamental advantages of temporal networks (2016, background): “For example, networks with heterogeneous degree distributions, ubiquitous in natural systems, require a high fraction of the nodes to be directly controlled [20].”
Control Centrality and Hierarchical Structure in Complex Networks (2012, methodology): “By combining tools from control theory and network science, we proposed an efficient methodology to identify the minimum sets of driver nodes, whose time-dependent control can guide the whole network to any desired final state [12].”
Smart Grids: A Cyber–Physical Systems Perspective (2016, background): “This may result in achieving close-to-ideal modeling or control effects focusing only on few drive nodes [61] or few smaller”
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks (2013, background): “The conclusions of Guimera et al. - and the need to formalise them in a paradigm such as LCP - appear clearer if interpreted in the light shed by two theories recently posited, one by Bogun˜a´ et al. 37 on the navigability of complex networks, and the other by Liu et al. 38 on the controllability of”
Network neuroscience (2017, background): “A particularly promising example of such a theory lies in network control, a nascent field of engineering that has its roots in the 1970s 110 and has been re-popularized more recently 111 .”
On Submodularity and Controllability in Complex Dynamical Networks (2014, methodology): “One issue with the approach taken by [26] and much of the follow-up work is that the exclusive focus on structural controllability and the associated quantitative notion of controllability (namely, the number/fraction of required driver nodes) can be rather crude and even misleading in some settings”
Controllability of Weighted and Directed Networks with Nonidentical Node Dynamics (2013, methodology): “[18] pointed out that the main results in [15] hinge on a critical modeling assumption: the results (implicitly) require that the “default” structures of the dynamical systems at the nodes of the network have infinite time constants, which do not reflect the dynamics of real physical and biological ”
Modelling dynamical processes in complex socio-technical systems (2011, background): “…theory offers a large set of mathematical tools for steering engineered and natural systems, we are just taking the first steps towards a full understanding of how the network heterogeneities influence our ability to control the network dynamicsandhowthenetworkevolutionimpactscontrollability 112 .”
Exact controllability of complex networks (2013, methodology): “The notion of structural controllability was then introduced by Lin [29] and was recently adopted to directed complex networks [34].”