Author(s): T. R. Kirkpatrick and D. Thirumalai
The routine transformation of a liquid, as it is rapidly cooled, resulting in glass formation, is remarkably complex. A theoretical explanation of the dynamics associated with this process has remained one of the major unsolved problems in condensed matter physics. The random first order transition ...
[Rev. Mod. Phys. 87, 183] Published Tue Mar 03, 2015
In the usual Achlioptas processes the smallest clusters of a few randomly chosen ones are selected to merge together at each step. The resulting aggregation process leads to the delayed birth of a giant cluster and the so-called explosive percolation transition showing a set of anomalous features. We explore a process with the opposite selection rule, in which the biggest clusters of the randomly chosen ones merge together. We develop a theory of this kind of percolation based on the Smoluchowski equation, find the percolation threshold, and describe the scaling properties of this continuous transition, namely, the critical exponents and amplitudes, and scaling functions. We show that, qualitatively, this transition is similar to the ordinary percolation one, though occurring in less connected systems.
Nature Physics 11, 201 (2015). doi:10.1038/nphys3287
Scientists involved in nuclear research before and after the end of the Second World War continue to be the subjects of historical and cultural fascination.
Network motifs are small building blocks of complex networks. Statistically significant motifs often perform network-specific functions. However, the precise nature of the connection between motifs and the global structure and function of networks remains elusive. Here we show that the global structure of some real networks is statistically determined by the probability of connections within motifs of size at most 3, once this probability accounts for node degrees. The connectivity profiles of node triples in these networks capture all their local and global properties. This finding impacts methods relying on motif statistical significance, and enriches our understanding of the elementary forces that shape the structure of complex networks.
We give several algebraic bounds for percolation on directed and undirected graphs: proliferation of strongly-connected clusters, proliferation of in- and out-clusters, and the transition associated with the number of giant components.
Synchronization is a universal phenomenon found in many non-equilibrium systems. Much recent interest in this area has overlapped with the study of complex networks, where a major focus is determining how a system's connectivity patterns affect the types of behavior that it can produce. Thus far, modeling efforts have focused on the tendency of networks of oscillators to mutually synchronize themselves, with less emphasis on the effects of external driving. In this work we discuss the interplay between mutual and driven synchronization in networks of phase oscillators of the Kuramoto type, and explore how the structure and emergence of such states depends on the underlying network topology for simple random networks with a given degree distribution. We find a variety of interesting dynamical behaviors, including bifurcations and bistability patterns that are qualitatively different for heterogeneous and homogeneous networks, and which are separated by a Takens-Bogdanov-Cusp singularity in the parameter region where the coupling strength between oscillators is weak. Our analysis is connected to the underlying dynamics of oscillator clusters for important states and transitions.
The topological structure of the power grid plays a key role in the reliable delivery of electricity and price settlement in the electricity market. Incorporation of new energy sources and loads into the grid over time has led to its structural and geographical expansion and can affect its stable operation. This paper presents an intuitive analytical model for the temporal evolution of large grids and uses it to understand common structural features observed in grids across America. In particular, key graph parameters like degree distribution, graph diameter, betweenness centralities, eigen-spread and clustering coefficients, as well as graph processes like infection propagation are used to quantify the model's benefits through comparison with the Western US and ERCOT power grids. The most significant contribution of the developed model is its analytical tractability, that provides a closed form expression for the nodal degree distribution observed in large grids. The discussed model can be used to generate realistic test cases to analyze topological effects on grid functioning and new grid technologies.
Author(s): Rafael S. Pinto and Alberto Saa
Networks of Kuramoto oscillators with a positive correlation between the oscillators frequencies and the degree of their corresponding vertices exhibit so-called explosive synchronization behavior, which is now under intensive investigation. Here we study and discuss explosive synchronization in a s...
[Phys. Rev. E 91, 022818] Published Fri Feb 27, 2015
Author(s): Narayan Puthanmadam Subramaniyam and Jari Hyttinen
Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and non...
[Phys. Rev. E 91, 022927] Published Fri Feb 27, 2015
Author(s): D. Angulo-Garcia and A. Torcini
We consider pulse-coupled leaky integrate-and-fire neural networks with randomly distributed synaptic couplings. This random dilution induces fluctuations in the evolution of the macroscopic variables and deterministic chaos at the microscopic level. Our main aim is to mimic the effect of the diluti...
[Phys. Rev. E 91, 022928] Published Fri Feb 27, 2015
Author(s): Yong Zou, Reik V. Donner, and Jürgen Kurths
Long-range correlated processes are ubiquitous, ranging from climate variables to financial time series. One paradigmatic example for such processes is fractional Brownian motion (fBm). In this work, we highlight the potentials and conceptual as well as practical limitations when applying the recent...
[Phys. Rev. E 91, 022926] Published Fri Feb 27, 2015
We introduce a framework to intertwine dynamical processes of different nature, each with its own distinct network topology, using a multilayer network approach. As an example of collective phenomena emerging from the interactions of multiple dynamical processes, we study a model where neural dynamics and nutrient transport are bidirectionally coupled in such a way that the allocation of the transport process at one layer depends on the degree of synchronization at the other layer, and vice versa. We show numerically, and we prove analytically, that the multilayer coupling induces a spontaneous explosive synchronization and a heterogeneous distribution of allocations, otherwise not present in the two systems considered separately. Our framework can find application to other cases where two or more dynamical processes such as synchronization, opinion formation, information diffusion, or disease spreading, are interacting with each other.
Author(s): Nirmal Punetha, Sangeeta Rani Ujjwal, Fatihcan M. Atay, and Ramakrishna Ramaswamy
We study a system of mismatched oscillators on a bipartite topology with time-delay coupling, and analyze the synchronized states. For a range of parameters, when all oscillators lock to a common frequency, we find solutions such that systems within a partition are in complete synchrony, while there...
[Phys. Rev. E 91, 022922] Published Thu Feb 26, 2015
Author(s): Arturo Buscarino, Mattia Frasca, Lucia Valentina Gambuzza, and Philipp Hövel
Chimera states have been recently found in a variety of different coupling schemes and geometries. In most cases, the underlying coupling structure is considered to be static, while many realistic systems display significant temporal changes in the pattern of connectivity. In this work we investigat...
[Phys. Rev. E 91, 022817] Published Thu Feb 26, 2015
In this paper, a statistical model for the coupling of electromagnetic radiation into enclosures through apertures is presented. The model gives a unified picture bridging deterministic theories of aperture radiation, and statistical models necessary for capturing the properties of irregular shaped enclosures. A Monte Carlo technique based on random matrix theory is used to predict and study the power transmitted through the aperture into the enclosure. Universal behavior of the net power entering the aperture is found. Results are of interest for predicting the coupling of external radiation through openings in irregular enclosures and reverberation chambers.
Author(s): E. Padmanaban, Stefano Boccaletti, and S. K. Dana
We evidence an interesting kind of hybrid synchronization in coupled chaotic systems where complete synchronization is restricted to only a subset of variables of two systems while other subset of variables may be in a phase synchronized state or desynchronized. Such hybrid synchronization is a gene...
[Phys. Rev. E 91, 022920] Published Tue Feb 24, 2015
One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts. In particular, in networks of identical nodes it has been shown that heterogeneity can lower the epidemic threshold at which epidemics can invade the system. Network heterogeneity can thus allow diseases with lower transmission probabilities to persist and spread. Here, we point out that for real world applications, this result should not be regarded independently of the intra-individual heterogeneity between people. Our results show that, if heterogeneity among people is taken into account, networks that are more heterogeneous in connectivity can be more resistant to epidemic spreading. We study a susceptible-infected-susceptible model with adaptive disease avoidance. Results from this model suggest that this reversal of the effect of network heterogeneity is likely to occur in populations in which the individuals are aware of their subjective disease risk. For epidemiology, this implies that network heterogeneity should not be studied in isolation.
We generalize the Kuramoto model of globally coupled oscillators to multifrequency communities. A situation when mean frequencies of two subpopulations are close to resonance 2:1 is considered in detail. We derive uniformly rotating solutions describing synchronization inside communities and between them. Remarkably, cross-coupling between the frequency scales can promote synchrony even when ensembles are separately asynchronous. We also show that the transition to synchrony due to the cross-coupling is accompanied by a huge multiplicity of distinct synchronous solutions what is directly related to a multi-branch entrainment. On the other hand, for synchronous populations, the cross-frequency coupling can destroy a phase-locking and lead to chaos of mean fields.
Author(s): Maycon S. Araújo, Fabio S. Vannucchi, André M. Timpanaro, and Carmen P. C. Prado
This paper studies the Sznajd model for opinion formation in a population connected through a general network. A master equation describing the time evolution of opinions is presented and solved in a mean-field approximation. Although quite simple, this approximation allows us to capture the most im...
[Phys. Rev. E 91, 022813] Published Mon Feb 23, 2015
Author(s): S. Hwang, S. Choi, Deokjae Lee, and B. Kahng
Mutually connected components (MCCs) play an important role as a measure of resilience in the study of interdependent networks. Despite their importance, an efficient algorithm to obtain the statistics of all MCCs during the removal of links has thus far been absent. Here, using a well-known fully d...
[Phys. Rev. E 91, 022814] Published Mon Feb 23, 2015
Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics) can impact on spatio-temporal neural activity and constrain the resulting dynamics.
We present a novel method to compute the phase space distribution in the nonequilibrium stationary state of a wide class of mean-field systems involving rotators subject to quenched disordered external drive and dissipation. The method involves a series expansion of the stationary distribution in inverse of the damping coefficient; the expansion coefficients satisfy recursion relations whose solution requires computing a sparse matrix, making numerical evaluation simple and efficient. We illustrate our method for the paradigmatic Kuramoto model of spontaneous collective synchronization and for its two mode generalization, in presence of noise and inertia, and demonstrate an excellent agreement between simulations and theory for the phase space distribution.