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Géza Ódor

Publications and source records attributed to Géza Ódor.

At least 19 recordsLinked to original sources

Slow activity decay in excitable models with discontinuous phase transitions

As a simple model of interacting systems of excitable degrees of freedom, we consider a threshold contact process on various lattices and networks, in which a successful activation event requires the presence of more than one active neighbors. We show by combining numerical simulations and a phenomenological theory that, in the two-phase coexistence region and for a sufficiently low initial activity, a slower-than-exponential temporal decay of the global density emerges, caused by the spontaneous formation of slowly vanishing and non-communicating clusters of activity. This slow-decay phenomenon is found to appear generally for regular lattices and also for finite-dimensional random lattices such as the Voronoi-Delaunay network. However, the slow-decay is impeded by small-world property, as demonstrated by simulations on random-regular networks. In the case of a power-law decay of the order parameter, which is valid among others on two-dimensional regular lattices, our phenomenological theory points out that the decaying state is stable against unbounded nucleation only if the decay exponent exceeds $1/2$. Besides the well-known Griffiths effects of quenched random systems, this slow-decay phenomenon rooted in the threshold condition provides an alternative mechanism of off-critical but scale-free dynamics, occurring also in the absence of disorder.

cond-mat.stat-mech↗

Fluctuation-dissipation of the Kuramoto model on fruit-fly connectomes

We investigate the distance from equilibrium using the Kuramoto model via the degree of fluctuation-dissipation violation as the consequence of different levels of edge weight anisotropies. This is achieved by solving the synchronization equations on the raw, homeostatic weighted and a random inhibitory edge variant of a real full fly (FF) connectome, containing $\simeq 10^5$ neuron cell nodes. We investigate these systems close to their synchronization transition critical points. While the topological(graph) dimension is high: $d \simeq 6$ the spectral dimensions of the variants, relevant in describing the synchronization behavior, are lower than the upper critical dimension: $d_s \simeq 2 < d_c=5$, suggesting relevant fluctuation effects and non mean-field scaling behavior. By measuring the auto-correlations and the auto-response functions for small perturbations we calculate the fluctuation-dissipation ratios (FDR) for the different variants of different anisotropy levels of the FF connectome. Numerical evidence is presented that the FDRs follow the level of anisotropy of these non-equilibrium systems in agreement with the expectations, with increasing temporal oscillations. Numerical values for the aging exponents provide confirmation for the predictions of the generalized time-translational-invariance theory. We compare the non-reciprocal connectome results with those on a symmetric Erd\H os-Rényi random graph of similar size and provide estimates for the Kuramoto model aging behavior. We also present a network analysis of the FF connectome and calculate the level of hierarchy, also related to the anisotropy. Finally, we provide some partial results for the periodically forced Shinomoto-Kuramoto model describing fluctuations in the non-resting state. We provide numerical evidence that fluctuations are maximal in the resting state of the critical brain.

cond-mat.dis-nn↗

The effect of HVDC lines in power-grids via Kuramoto modelling

We present a numerical study on the synchronization and cascade failure behaviour by solving the adaptive second-order Kuramoto model on a large high voltage (HV) European power-grid. This non-perturbative analysis takes into account non-linear effects, which occur even when phase differences are large, when the system is away from the steady state, and even during a blackout cascade. Our dynamical simulations show that improvements in the phase synchronziation stabilization as well as the in the cascade sizes can be related to the finite size scaling behaviour of the second order Kuramoto on graphs with $d_s<4$ spectral dimensions. On the other hand drawbacks in the frequency spread and Braess effects also occur by varying the total transmitted power at large and small global couplings, presumably when the fluctuations are small, causing a freezing in the dynamics. We compare simulations of the fully AC model with those of static or adaptive High Voltage Direct Current (HVDC) line replacements. The adaptive (local frequency difference-based) HVDC lines are more efficient in the steady state, at the expense of very long relaxation times.

physics.soc-ph↗

Quantitative comparison of power grid reinforcements

This paper presents a quantitative comparison of power grid reinforcement strategies. We evaluate three approaches: (1) doubling transmission links (bridges) between different communities, (2) adding bypasses around weakly synchronized nodes, and (3) reinforcing edges that trigger the largest cascade failures. We use two different models of the Hungarian high-voltage network. These models are built from the official data provided by the transmission system operator, thus eliminating the assumptions typically used in other studies. The coupling strength distribution of the Hungarian models shows good agreement with our previous works using the European and North American grids. Additionally, we examine the occurrence of Braess' paradox, where added transmission capacity unexpectedly reduces overall stability. Our results show that reinforcement through community-based bridge duplication yields the most significant improvements across all parameters. A visual comparison highlights differences between this method and traditional reinforcement approaches. To the authors' knowledge, this is the first attempt to quantitatively compare results of oscillator-based studies with those relying on power system analysis software. Characteristic results of line-cut simulations reveal cascade size distributions with fat-tailed decays for medium coupling strengths, while exponential behavior emerges for small and large couplings. The observed exponents are reminiscent of the continuously changing exponents due to Griffiths effects near a hybrid type of phase transition.

cond-mat.dis-nn↗

Scale-free behavior of weight distributions of connectomes

To determine the precise link between anatomical structure and function, brain studies primarily concentrate on the anatomical wiring of the brain and its topological properties. In this work, we investigate the weighted degree and connection length distributions of the KKI-113 and KKI-18 human connectomes, the fruit fly, and of the mouse retina. We found that the node strength (weighted degree) distribution behavior differs depending on the considered scale. On the global scale, the distributions are found to follow a power-law behavior, with a roughly universal exponent close to 3. However, this behavior breaks at the local scale as the node strength distributions of the KKI-18 follow a stretched exponential, and the fly and mouse retina follow the lognormal distribution, respectively which are indicative of underlying random multiplicative processes and underpins non-locality of learning in a brain close to the critical state. However, for the case of the KKI-113 and the H01 human (1mm$^3$) datasets, the local weighted degree distributions follow an exponentially truncated power-law, which may hint at the fact that the critical learning mechanism may have manifested at the node level too.

cond-mat.dis-nn↗

Studying power-grid synchronization with incremental refinement of model heterogeneity

Modeling power-grid systems has got a major importance in present days as transformation to renewable energy sources requires the complete re-design of energy transmission. Renewable energy sources can be located quite far from their consumption points because urban and industrial structures do not follow physical constraints and capabilities. Important examples are the sea coast vs inland divisions in the case of wind power. Ill-constructed high-voltage (HV) power grids can cause catastrophic damages to economies as it was demonstrated in recent history via the emergence of large blackout events. The probability distributions of such events was found to be fat-tailed, exhibiting power-law (PL) tails very often. To understand them, self-organized critical direct current (DC) models have been constructed~\cite{car2} and have been shown to describe well the PL exponents of empirical values. However, many details could not be understood as power-grids work with alternating currents (AC) in which phase differences are the primary causes of the power-flows.

nlin.AO↗

Finite-size scaling and dynamics in a two-dimensional lattice of identical oscillators with frustrated couplings

A two-dimensional lattice of oscillators with identical (zero) intrinsic frequencies and Kuramoto type of interactions with randomly frustrated couplings is considered. Starting the time evolution from slightly perturbed synchronized states, we study numerically the relaxation properties, as well as properties at the stable fixed point which can also be viewed as a metastable state of the closely related XY spin glass model. According to our results, the order parameter at the stable fixed point shows generally a slow, reciprocal logarithmic convergence to its limiting value with the system size. The infinite-size limit is found to be close to zero for zero-centered Gaussian couplings, whereas, for a binary $\pm 1$ distribution with a sufficiently high concentration of positive couplings, it is significantly above zero. Besides, the relaxation time is found to grow algebraically with the system size. Thus, the order parameter in an infinite system approaches its limiting value inversely proportionally to $\ln t$ at late times $t$, similarly to that found in the model with all-to-all couplings [Daido, Chaos {\bf 28}, 045102 (2018)]. As opposed to the order parameter, the energy of the corresponding XY model is found to converge algebraically to its infinite-size limit.

cond-mat.dis-nn↗

Chimera states in neural networks and power systems

Partial, frustrated synchronization and chimera-like states are expected to occur in Kuramoto-like models if the spectral dimension of the underlying graph is low: $d_s < 4$. We provide numerical evidence that this really happens in case of the high-voltage power grid of Europe ($d_s < 2$), a large human connectome (KKI113) and in case of the largest, exactly known brain network corresponding to the fruit-fly (FF) connectome ($d_s < 4$), even though their graph dimensions are much higher, i.e.: $d^{EU}_g\simeq 2.6(1)$ and $d^{FF}_g\simeq 5.4(1)$, $d^{\mathrm{KKI113}}_g\simeq 3.4(1)$. We provide local synchronization results of the first- and second-order (Shinomoto) Kuramoto models by numerical solutions on the FF and the European power-grid graphs, respectively, and show the emergence of \red{chimera-like} patterns on the graph community level as well as by the local order parameters.

cond-mat.stat-mech↗

Improving power-grid systems via topological changes, or how self-organized criticality can help stability

Cascade failures in power grids occur when the failure of one component or subsystem causes a chain reaction of failures in other components or subsystems, ultimately leading to a widespread blackout or outage. Controlling cascade failures on power grids is important for many reasons like economic impact, national security, public safety and even rippled effects like troubling transportation systems. Monitoring the networks on node level has been suggested by many, either controlling all nodes of a network or by subsets. This study identifies sensitive graph elements of the weighted European power-grids (from 2016, 2022) by two different methods. Bridges are determined between communities and "weak" nodes are selected by the lowest local synchronization of the swing equation. In the latter case we add bypasses of the same number as the bridges at weak nodes, and we compare the synchronization, cascade failure behavior by the dynamical improvement with the purely topological changes. The results are also compared if bridges are removed from networks, which results in a case similar to islanding, and with the addition of links at randomly selected places. Bypassing was found to improve synchronization the best, while the average cascade sizes are the lowest with bridge additions. However, for very large or small global couplings these network changes do not help, they seem to be useful near the synchronization transition region, where self-organization drives the power-grid. Thus, we provide a demonstration for the Braess' Paradox on continent-sized power grid simulations and uncover the limitations of this phenomenon. We also determine the cascade size distributions and justify the power-law tails near the transition point on these grids.

physics.soc-ph↗

Dynamical heterogeneity and universality of power-grids

While weak, tuned asymmetry can improve, strong heterogeneity destroys synchronization in the electric power system. We study the level of heterogeneity, by comparing large high voltage (HV) power-grids of Europe and North America. We provide an analysis of power capacities and loads of various energy sources from the databases and found heavy tailed distributions with similar characteristics. Graph topological measures, community structures also exhibit strong similarities, while the cable admittance distributions can be well fitted with the same power-laws (PL), related to the length distributions. The community detection analysis shows the level of synchronization in different domains of the European HV power grids, by solving a set of swing equations. We provide numerical evidence for frustrated synchronization and Chimera states and point out the relation of topology and level of synchronization in the subsystems. We also provide empirical data analysis of the frequency heterogeneities within the Hungarian HV network and find q-Gaussian distributions related to super-statistics of time-lagged fluctuations, which agree well with former results on the Nordic Grid.

physics.soc-ph↗

Revisiting and modeling power-law distributions in empirical outage data of power systems

The size distribution of planned and forced outages and following restoration times in power systems have been studied for almost two decades and has drawn great interest as they display heavy tails. Understanding of this phenomenon has been done by various threshold models, which are self-tuned at their critical points, but as many papers pointed out, explanations are intuitive, and more empirical data is needed to support hypotheses. In this paper, the authors analyze outage data collected from various public sources to calculate the outage energy and outage duration exponents of possible power-law fits. Temporal thresholds are applied to identify crossovers from initial short-time behavior to power-law tails. We revisit and add to the possible explanations of the uniformness of these exponents. By performing power spectral analyses on the outage event time series and the outage duration time series, it is found that, on the one hand, while being overwhelmed by white noise, outage events show traits of self-organized criticality (SOC), which may be modeled by a crossover from random percolation to directed percolation branching process with dissipation, coupled to a conserved density. On the other hand, in responses to outages, the heavy tails in outage duration distributions could be a consequence of the highly optimized tolerance (HOT) mechanism, based on the optimized allocation of maintenance resources.

cond-mat.stat-mech↗

Synchronization transitions on connectome graphs with external force

We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the fruit-fly and two large human connectomes. This model contains a force term, thus is capable of describing critical behavior in the presence of external excitation. By numerical solution we determine the crackling noise durations with and without thermal noise and show extended non-universal scaling tails characterized by $2< τ_t < 2.8$, in contrast with the Hopf transition of the Kuramoto model, without the force $τ_t=3.1(1)$. Comparing the phase and frequency order parameters we find different transition points and fluctuations peaks as in case of the Kuramoto model. Using the local order parameter values we also determine the Hurst (phase) and $β$ (frequency) exponents and compare them with recent experimental results obtained by fMRI. We show that these exponents, characterizing the auto-correlations are smaller in the excited system than in the resting state and exhibit module dependence.

cond-mat.dis-nn↗

Critical behavior of the diffusive susceptible-infected-recovered model

The critical behavior of the non-diffusive susceptible-infected-recovered model on lattices had been well established in virtue of its duality symmetry. By performing simulations and scaling analyses for the diffusive variant on the two-dimensional lattice, we show that diffusion for all agents, while rendering this symmetry destroyed, constitutes a singular perturbation that induces asymptotically distinct dynamical and stationary critical behavior from the non-diffusive model. In particular, the manifested crossover behavior in the effective mean-square radius exponents reveals that slow crossover behavior in general diffusive multi-species reaction systems may be ascribed to the interference of multiple length scales and timescales at early times.

cond-mat.stat-mech↗

Synchronization transition of the second-order Kuramoto model on lattices

The second-order Kuramoto equation describes synchronization of coupled oscillators with inertia, which occur in power grids for example. Contrary to the first-order Kuramoto equation it's synchronization transition behavior is much less known. In case of Gaussian self-frequencies it is discontinuous, in contrast to the continuous transition for the first-order Kuramoto equation. Here we investigate this transition on large 2d and 3d lattices and provide numerical evidence of hybrid phase transitions, that the oscillator phases $θ_i$, exhibit a crossover, while the frequency spread a real phase transition in 3d. Thus a lower critical dimension $d_l^O=2$ is expected for the frequencies and $d_l^R=4$ for the phases like in the massless case. We provide numerical estimates for the critical exponents, finding that the frequency spread decays as $\sim t^{-d/2}$ in case of aligned initial state of the phases in agreement with the linear approximation. However in 3d, in the case of initially random distribution of $θ_i$, we find a faster decay, characterized by $\sim t^{-1.8(1)}$ as the consequence of enhanced nonlinearities which appear by the random phase fluctuations.

cond-mat.stat-mech↗

Synchronization dynamics on the EU and US power grids

Dynamical simulation of the cascade failures on the EU and USA high-voltage power grids has been done via solving the second-order Kuramoto equation. We show that synchronization transition happens by increasing the global coupling parameter $K$ with metasatble states depending on the initial conditions so that hysteresis loops occur. We provide analytic results for the time dependence of frequency spread in the large $K$ approximation and by comparing it with numerics of $d=2,3$ lattices, we find agreement in the case of ordered initial conditions. However, different power-law (PL) tails occur, when the fluctuations are strong. After thermalizing the systems we allow a single line cut failure and follow the subsequent overloads with respect to threshold values $T$. The PDFs $p(N_f)$ of the cascade failures exhibit PL tails near the synchronization transition point $K_c$. Near $K_c$ the exponents of the PL-s for the US power grid vary with $T$ as $1.4 \le τ\le 2.1$, in agreement with the empirical blackout statistics, while on the EU power grid we find somewhat steeper PL-s characterized by $1.4 \le τ\le 2.4$. Below $K_c$ we find signatures of $T$-dependent PL-s, caused by frustrated synchronization, reminiscent of Griffiths effects. Here we also observe stability growth following the blackout cascades, similar to intentional islanding, but for $K > K_c$ this does not happen. For $T < T_c$, bumps appear in the PDFs with large mean values, known as "dragon king" blackout events. We also analyze the delaying/stabilizing effects of instantaneous feedback or increased dissipation and show how local synchronization behaves on geographic maps.

cond-mat.stat-mech↗

What makes us humans: Differences in the critical dynamics underlying the human and fruit-fly connectome

Previous simulation studies on human connectomes suggested, that critical dynamics emerge subcrititcally in the so called Griffiths Phases. %This is the consequence of the strong heterogeneity of the graphs. Now we investigate this on the largest available brain network, the $21.662$ node fruit-fly connectome, using the Kuramoto synchronization model. As this graph is less heterogeneous, lacking modular structure and exhibit high topological dimension, we expect a difference from the previous results. Indeed, the synchronization transition is mean-field like, and the width of the transition region is larger than in random graphs, but much smaller than as for the KKI-18 human connectome. This demonstrates the effect of modular structure and dimension on the dynamics, providing a basis for better understanding the complex critical dynamics of humans.

q-bio.NC↗

Modelling on the very large-scale connectome

In this review, we discuss critical dynamics of simple nonequilibrium models on large connectomes, obtained by diffusion MRI, representing the white matter of the human brain. In the first chapter, we overview graph theoretical and topological analysis of these networks, pointing out that universality allows selecting a representative network, the KKI-18, which has been used for dynamical simulation. The critical and sub-critical behaviour of simple, two- or three-state threshold models is discussed with special emphasis on rare-region effects leading to robust Griffiths Phases (GP). Numerical results of synchronization phenomena, studied by the Kuramoto model, are also shown, leading to a continuous analog of the GP, termed frustrated synchronization. The models presented here exhibit dynamical scaling behaviour with exponents in agreement with brain experimental data if local homeostasis is provided.

cond-mat.stat-mech↗

Non-universal power-law dynamics of SIR models on hierarchical modular networks

Power-law (PL) time dependent infection growth has been reported in many COVID-19 statistics. In simple SIR models the number of infections grows at the outbreak as $I(t) \propto t^{d-1}$ on $d$-dimensional Euclidean lattices in the endemic phase or follow a slower universal PL at the critical point, until finite sizes cause immunity and a crossover to an exponential decay. Heterogeneity may alter the dynamics of spreading models, spatially inhomogeneous infection rates can cause slower decays, posing a threat of a long recovery from a pandemic. COVID-19 statistics have also provided epidemic size distributions with PL tails in several countries. Here I investigate SIR like models on hierarchical modular networks, embedded in 2d lattices with the addition of long-range links. I show that if the topological dimension of the network is finite, average degree dependent PL growth of prevalence emerges. Supercritically the same exponents as of regular graphs occurs, but the topological disorder alters the critical behavior. This is also true for the epidemic size distributions. Mobility of individuals does not affect the form of the scaling behavior, except for the $d=2$ lattice, but increases the magnitude of the epidemic. The addition of a super-spreader hot-spot also does not change the growth exponent and the exponential decay in the herd immunity regime.

cond-mat.dis-nn↗