Functionability in complex networks: Leading nodes for the transition from structural to functional networks through remote asynchronization.

Complex networks are essentially heterogeneous not only in the basic properties of the constituent nodes, such as their degree, but also in the effects that these have on the global dynamical properties of the network. Networks of coupled identical phase oscillators are good examples for analyzing these effects, since an overall synchronized state can be considered a reference state. A small variation of intrinsic node parameters may cause the system to move away from synchronization, and a new phase-locked stationary state can be achieved. We propose a measure of phase dispersion that quantifies the functional response of the system to a given local perturbation. As a particular implementation, we propose a variation of the standard Kuramoto model in which the nodes of a complex network interact with their neighboring nodes, by including a node-dependent frustration parameter. The final stationary phase-locked state now depends on the particular frustration parameter at each node and also on the network topology. We exploit this scenario by introducing individual frustration parameters and measuring what their effect on the whole network is, measured in terms of the phase dispersion, which depends only on the topology of the network and on the choice of the particular node that is perturbed. This enables us to define a characteristic of the node, its functionability, that can be computed analytically in terms of the network topology. Finally, we provide a thorough comparison with other centrality measures.

[1]  C. Barnes,et al.  Neural plasticity in the ageing brain , 2006, Nature Reviews Neuroscience.

[2]  Lucas C Parra,et al.  Finding influential nodes for integration in brain networks using optimal percolation theory , 2018, Nature Communications.

[3]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[4]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[5]  Yaneer Bar-Yam,et al.  The Statistical Mechanics of Complex Product Development: Empirical and Analytical Results , 2007, Manag. Sci..

[6]  Athen Ma,et al.  Rich-Cores in Networks , 2014, PloS one.

[7]  D. Povh,et al.  A nonlinear control for coordinating TCSC and generator excitation to enhance the transient stability of long transmission systems , 2001 .

[8]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[9]  R. Merris Laplacian matrices of graphs: a survey , 1994 .

[10]  Alex Arenas,et al.  Synchronization reveals topological scales in complex networks. , 2006, Physical review letters.

[11]  Jean M. Vettel,et al.  Controllability of structural brain networks , 2014, Nature Communications.

[12]  Danielle S Bassett,et al.  Developmental increases in white matter network controllability support a growing diversity of brain dynamics , 2016, Nature Communications.

[13]  S. Battiston,et al.  The power to control , 2013, Nature Physics.

[14]  A. Hamdan,et al.  Measures of Modal Controllability and Observability for First- and Second-Order Linear Systems , 1989 .

[15]  Flaviano Morone,et al.  Model of brain activation predicts the neural collective influence map of the brain , 2017, Proceedings of the National Academy of Sciences.

[16]  Francesco Sorrentino,et al.  Cluster synchronization and isolated desynchronization in complex networks with symmetries , 2013, Nature Communications.

[17]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[18]  A. Arenas,et al.  Synchronization processes in complex networks , 2006, nlin/0610057.

[19]  Emanuele Cozzo,et al.  A Complex Network Framework to Model Cognition: Unveiling Correlation Structures from Connectivity , 2018, Complex..

[20]  Jürgen Kurths,et al.  Synchronization: Phase locking and frequency entrainment , 2001 .

[21]  M. Wolfrum,et al.  Nonuniversal transitions to synchrony in the Sakaguchi-Kuramoto model. , 2012, Physical review letters.

[22]  Martin G. Everett,et al.  Models of core/periphery structures , 2000, Soc. Networks.

[23]  Kumpati S. Narendra,et al.  Control of nonlinear dynamical systems using neural networks: controllability and stabilization , 1993, IEEE Trans. Neural Networks.

[24]  Klaus-Peter Lesch,et al.  Serotonin in the Modulation of Neural Plasticity and Networks: Implications for Neurodevelopmental Disorders , 2012, Neuron.

[25]  Klaus Lehnertz,et al.  Identifying important nodes in weighted functional brain networks: a comparison of different centrality approaches. , 2012, Chaos.

[26]  Marcus Kaiser,et al.  Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems , 2006, PLoS Comput. Biol..

[27]  Daniel M Abrams,et al.  Symmetry-broken states on networks of coupled oscillators. , 2016, Physical review. E.

[28]  S. Strogatz Exploring complex networks , 2001, Nature.

[29]  Phillip Bonacich,et al.  Some unique properties of eigenvector centrality , 2007, Soc. Networks.

[30]  Y. Kuramoto,et al.  A Soluble Active Rotater Model Showing Phase Transitions via Mutual Entertainment , 1986 .

[31]  Ding-wei Huang,et al.  Traffic signal synchronization. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[33]  Mason A. Porter,et al.  Core-Periphery Structure in Networks (Revisited) , 2017, SIAM Rev..

[34]  Jun Ye,et al.  Cosine similarity measures for intuitionistic fuzzy sets and their applications , 2011, Math. Comput. Model..

[35]  Sang Hoon Lee,et al.  Detection of core–periphery structure in networks using spectral methods and geodesic paths , 2014, European Journal of Applied Mathematics.

[36]  M. Brede,et al.  Frustration tuning and perfect phase synchronization in the Kuramoto-Sakaguchi model. , 2016, Physical review. E.

[37]  Ruben Schmidt,et al.  Kuramoto model simulation of neural hubs and dynamic synchrony in the human cerebral connectome , 2015, BMC Neuroscience.

[38]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[39]  O. Sporns,et al.  From regions to connections and networks: new bridges between brain and behavior , 2016, Current Opinion in Neurobiology.

[40]  Desmond J. Higham,et al.  Network Properties Revealed through Matrix Functions , 2010, SIAM Rev..

[41]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[42]  S Arianos,et al.  Power grid vulnerability: a complex network approach. , 2008, Chaos.

[43]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[44]  Olaf Sporns,et al.  Generative models of the human connectome , 2015, NeuroImage.

[45]  O. Sporns Structure and function of complex brain networks , 2013, Dialogues in clinical neuroscience.

[46]  R. Spigler,et al.  The Kuramoto model: A simple paradigm for synchronization phenomena , 2005 .

[47]  Réka Albert,et al.  Structural vulnerability of the North American power grid. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Alex Arenas,et al.  Paths to synchronization on complex networks. , 2006, Physical review letters.

[49]  A. Lombardi,et al.  Controllability analysis of networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[50]  D S Callaway,et al.  Network robustness and fragility: percolation on random graphs. , 2000, Physical review letters.

[51]  Kristina Lerman,et al.  The interplay between dynamics and networks: centrality, communities, and cheeger inequality , 2014, KDD.

[52]  Michele Benzi,et al.  On the Limiting Behavior of Parameter-Dependent Network Centrality Measures , 2013, SIAM J. Matrix Anal. Appl..

[53]  Jurgen Kurths,et al.  Synchronization in complex networks , 2008, 0805.2976.

[54]  A. Díaz-Guilera,et al.  Synchronization and modularity in complex networks , 2007 .

[55]  L Fortuna,et al.  Remote synchronization in star networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[56]  Vito Latora,et al.  Remote synchronization reveals network symmetries and functional modules. , 2012, Physical review letters.