arXiv subjects

Hanshuang Chen

Publications and source records attributed to Hanshuang Chen.

At least 37 records · Page 2Linked to original sources

arXiv2020

Effect of overlap on spreading dynamics on multiplex networks

In spite of the study of epidemic dynamics on single-layer networks has received considerable attention, the epidemic dynamics on multiplex networks is still limited and is facing many challenges. In this work, we consider the susceptible-infected-susceptible-type (SIS) epidemic model on multiplex networks and investigate the effect of overlap among layers on the spreading dynamics. To do so, we assume that the prerequisite of one $S$-node to be infected is that there is at least one infectious neighbor in each layer. A remarkable result is that the overlap can alter the nature of the phase transition for the onset of epidemic outbreak. Specifically speaking, the system undergoes a usual continuous phase transition when two layers are completely overlapped. Otherwise, a discontinuous phase transition is observed, accompanied by the occurrence of a bistable region in which a disease-free phase and an endemic phase are coexisting. As the degree of the overlap decreases, the bistable region is enlarged. The results are validated by both simulation and mean-field theory.

cond-mat.stat-mech↗

arXiv2019

Spontaneous symmetry breaking and discontinuous phase transition for spreading dynamics in multiplex networks

We propose a spreading model in multilayer networks and study the nature of nonequilibrium phase transition in the model. The model integrates the susceptible-infected-susceptible (or susceptible-infected-recovered) spreading dynamics with a biased diffusion process among different layers. A parameter $α$ is introduced to control the bias of the diffusion process, such that each individual prefers to move to one layer with more infected (or recovered) neighbors for larger values of $α$. Using stochastic simulations and mean-field theory, we show that the type of phase transition from a disease-free phase to an endemic phase depends on the value of $α$. When $α$ is small enough, the system undergoes a usual continuous phase transition as an effective spreading rate $β$ increases, as in single-layer networks. Interestingly, when $α$ exceeds a critical value the system shows either a hybrid two-step phase transition or a one-step discontinuous phase transition as $β$ increases. The former contains a continuous transition between the disease-free phase and a low-prevalence endemic phase, and a discontinuous transition between the low-prevalence endemic phase and a high-prevalence endemic phase. For the latter, only a discontinuous transition occurs from the disease-free phase directly to the high-prevalence endemic phase. Moreover, we show that the discontinuous transition is always accompanied by a spontaneous symmetry breaking in occupation probabilities of individuals in each layer.

physics.soc-ph↗

arXiv2018

Majority vote model with ancillary noise in complex networks

We analyze the properties of the majority-vote (MV) model with an additional noise in which a local spin can be changed independently of its neighborhood. In the standard MV, one of the simplest nonequilibrium systems exhibiting an order-disorder phase transition, spins are aligned with their local majority with probability $1-f$, and with complementary probability $f$, the majority rule is not followed. In the noisy MV (NMV), a random spin flip is succeeded with probability $p$ (with complementary $1-p$ the usual MV rule is accomplished). Such extra ingredient was considered by Vieira and Crokidakis [Physica A {\bf 450}, 30 (2016)] for the square lattice. Here, we generalize the NMV for arbitrary networks, including homogeneous [random regular (RR) and Erdös Renyi (ER)] and heterogeneous [Barabasi-Albert (BA)] structures, through mean-field calculations and numerical simulations. Results coming from both approaches are in excellent agreement with each other, revealing that the presence of additional noise does not affect the classification of phase transition, which remains continuous irrespective of the network degree and its distribution. The critical point and the threshold probability $p_t$ marking the disappearance of the ordered phase depend on the node distribution and increase with the connectivity $k$. The critical behavior, investigated numerically, exhibits a common set of critical exponents for RR and ER topologies, but different from BA and regular lattices. Finally, our results indicate that (in contrary to a previous proposition) there is no first-order transition in the NMV for large $k$.

cond-mat.stat-mech↗

arXiv2018

Double phase transition of the Ising model in core-periphery networks

We study the phase transition of the Ising model in networks with core-periphery structures. By Monte Carlo simulations, we show that prior to the order-disorder phase transition the system organizes into an inhomogeneous intermediate phase in which core nodes are much more ordered than peripheral nodes. Interestingly, the susceptibility shows double peaks at two distinct temperatures. We find that, if the connections between core and periphery increase linearly with network size, the first peak does not exhibit any size-dependent effect, and the second one diverges in the limit of infinite network size. Otherwise, if the connections between core and periphery scale sub-linearly with the network size, both peaks of the susceptibility diverge as power laws in the thermodynamic limit. This suggests the appearance of a double transition phenomenon in the Ising model for the latter case. Moreover, we develop a mean-field theory that agrees well with the simulations.

cond-mat.stat-mech↗

arXiv2017

Quenched mean-field theory for the majority-vote model on complex networks

The majority-vote (MV) model is one of the simplest nonequilibrium Ising-like model that exhibits a continuous order-disorder phase transition at a critical noise. In this paper, we present a quenched mean-field theory for the dynamics of the MV model on networks. We analytically derive the critical noise on arbitrary quenched unweighted networks, which is determined by the largest eigenvalue of a modified network adjacency matrix. By performing extensive Monte Carlo simulations on synthetic and real networks, we find that the performance of the quenched mean-field theory is superior to a heterogeneous mean-field theory proposed in a previous paper [Chen \emph{et al.}, Phys. Rev. E 91, 022816 (2015)], especially for directed networks.

cond-mat.stat-mech↗

arXiv2017

Large deviation induced phase switch in an inertial majority-vote model

We theoretically study noise-induced phase switch phenomena in an inertial majority-vote (IMV) model introduced in a recent paper [Phys. Rev. E 95, 042304 (2017)]. The IMV model generates a strong hysteresis behavior as the noise intensity $f$ goes forward and backward, a main characteristic of a first-order phase transition, in contrast to a second-order phase transition in the original MV model. Using the Wentzel-Kramers-Brillouin approximation for the master equation, we reduce the problem to finding the zero-energy trajectories in an effective Hamiltonian system, and the mean switching time depends exponentially on the associated action and the number of particles $N$. Within the hysteresis region, we find that the actions along the optimal forward switching path from ordered phase (OP) to disordered phase (DP) and its backward path, show distinct variation trends with $f$, and intersect at $f=f_c$ that determines the coexisting line of OP and DP. This results in a nonmonotonic dependence of the mean switching time between two symmetric OPs on $f$, with a minimum at $f_c$ for sufficiently large $N$. Finally, the theoretical results are validated by Monte Carlo simulations.

cond-mat.stat-mech↗

arXiv2017

Optimal Allocation of Resources for Suppressing Epidemic Spreading on Networks

Efficient allocation of limited medical resources is crucial for controlling epidemic spreading on networks. Based on the susceptible-infected-susceptible model, we solve an optimization problem as how best to allocate the limited resources so as to minimize the prevalence, providing that the curing rate of each node is positively correlated to its medical resource. By quenched mean-field theory and heterogeneous mean-field (HMF) theory, we prove that epidemic outbreak will be suppressed to the greatest extent if the curing rate of each node is directly proportional to its degree, under which the effective infection rate $λ$ has a maximal threshold $λ_c^{opt}=1/\left\langle k \right\rangle$ where $\left\langle k \right\rangle$ is average degree of the underlying network. For weak infection region ($λ\gtrsimλ_c^{opt}$), we combine a perturbation theory with Lagrange multiplier method (LMM) to derive the analytical expression of optimal allocation of the curing rates and the corresponding minimized prevalence. For general infection region ($λ>λ_c^{opt}$), the high-dimensional optimization problem is converted into numerically solving low-dimensional nonlinear equations by the HMF theory and LMM. Counterintuitively, in the strong infection region the low-degree nodes should be allocated more medical resources than the high-degree nodes to minimize the prevalence. Finally, we use simulated annealing to validate the theoretical results.

q-bio.PE↗

arXiv2016

Epidemic extinction in a generalized susceptible-infected-susceptible model

We study the extinction of epidemics in a generalized susceptible-infected-susceptible model, where a susceptible individual becomes infected with the rate $λ$ when contacting $m$ infective individual(s) simultaneously, and an infected individual spontaneously recovers with the rate $μ$. By employing the Wentzel-Kramers-Brillouin approximation for the master equation, the problem is reduced to finding the zero-energy trajectories in an effective Hamiltonian system, and the mean extinction time $\langle T\rangle$ depends exponentially on the associated action $\mathcal {S}$ and the size of the population $N$, $\langle T\rangle \sim \exp(N\mathcal {S})$. Because of qualitatively different bifurcation features for $m=1$ and $m\geq2$, we derive independently the expressions of $\mathcal {S}$ as a function of the rescaled infection rate $λ/μ$. For the weak infection, $\mathcal {S}$ scales to the distance to the bifurcation with an exponent $2$ for $m=1$ and $3/2$ for $m\geq2$. Finally, a rare-event simulation method is used to validate the theory.

q-bio.PE↗

arXiv2016

Critical noise of majority-vote model on complex networks

The majority-vote model with noise is one of the simplest nonequilibrium statistical model that has been extensively studied in the context of complex networks. However, the relationship between the critical noise where the order-disorder phase transition takes place and the topology of the underlying networks is still lacking. In the paper, we use the heterogeneous mean-field theory to derive the rate equation for governing the model's dynamics that can analytically determine the critical noise $f_c$ in the limit of infinite network size $N\rightarrow \infty$. The result shows that $f_c$ depends on the ratio of ${\left\langle k \right\rangle }$ to ${\left\langle k^{3/2} \right\rangle }$, where ${\left\langle k \right\rangle }$ and ${\left\langle k^{3/2} \right\rangle }$ are the average degree and the $3/2$ order moment of degree distribution, respectively. Furthermore, we consider the finite size effect where the stochastic fluctuation should be involved. To the end, we derive the Langevin equation and obtain the potential of the corresponding Fokker-Planck equation. This allows us to calculate the effective critical noise $f_c(N)$ at which the susceptibility is maximal in finite size networks. We find that the $f_c-f_c(N)$ decays with $N$ in a power-law way and vanishes for $N\rightarrow \infty$. All the theoretical results are confirmed by performing the extensive Monte Carlo simulations in random $k$-regular networks, Erdös-Rényi random networks and scale-free networks.

physics.soc-ph↗

arXiv2016

Explosive Phase Transition in a Majority-Vote Model with Inertia

We generalize the original majority-vote model by incorporating an inertia into the microscopic dynamics of the spin flipping, where the spin-flip probability of any individual depends not only on the states of its neighbors, but also on its own state. Surprisingly, the order-disorder phase transition is changed from a usual continuous type to a discontinuous or an explosive one when the inertia is above an appropriate level. A central feature of such an explosive transition is a strong hysteresis behavior as noise intensity goes forward and backward. Within the hysteresis region, a disordered phase and two symmetric ordered phases are coexisting and transition rates between these phases are numerically calculated by a rare-event sampling method. A mean-field theory is developed to analytically reveal the property of this phase transition.

physics.soc-ph↗

arXiv2016

Discontinuous phase transition in an annealed multi-state majority-vote model

In this paper, we generalize the original majority-vote (MV) model with noise from two states to arbitrary $q$ states, where $q$ is an integer no less than two. The main emphasis is paid to the comparison on the nature of phase transitions between the two-state MV (MV2) model and the three-state MV (MV3) model. By extensive Monte Carlo simulation and mean-field analysis, we find that the MV3 model undergoes a discontinuous order-disorder phase transition, in contrast to a continuous phase transition in the MV2 model. A central feature of such a discontinuous transition is a strong hysteresis behavior as noise intensity goes forward and backward. Within the hysteresis region, the disordered phase and ordered phase are coexisting.

cond-mat.stat-mech↗

arXiv2016

A hybrid multiscale coarse-grained method for dynamics on complex networks

Brute-force simulations for dynamics on very large networks are quite expensive. While phenomenological treatments may capture some macroscopic properties, they often ignore important microscopic details. Fortunately, one may be only interested in the property of local part and not in the whole network. Here, we propose a hybrid multiscale coarse-grained(HMCG) method which combines a fine Monte Carlo(MC) simulation on the part of nodes of interest with a more coarse Langevin dynamics on the rest part. We demonstrate the validity of our method by analyzing the equilibrium Ising model and the nonequilibrium susceptible-infected-susceptible model. It is found that HMCG not only works very well in reproducing the phase transitions and critical phenomena of the microscopic models, but also accelerates the evaluation of dynamics with significant computational savings compared to microscopic MC simulations directly for the whole networks. The proposed method is general and can be applied to a wide variety of networked systems just adopting appropriate microscopic simulation methods and coarse graining approaches.

physics.soc-ph↗

arXiv2015

Heterogeneous nucleation on complex networks with mobile impurities

We study the heterogeneous nucleation of Ising model on complex networks under a non-equilibrium situation where the impurities perform degree-biased motion controlled by a parameter α. Through the forward flux sampling and detailed analysis on the nucleating clusters, we find that the nucleation rate shows a nonmonotonic dependence on αfor small number of impurities, in which a maximal nucleation rate occurs at α=0 corresponding to the degree-uncorrelated random motion. Furthermore, we demonstrate the distinct features of the nucleating clusters along the pathway for different preference of impurities motion, which may be used to understand the resonance-like dependence of nucleation rate on the motion bias of impurities. Our theoretical analysis shows that the nonequilibrium diffusion of impurities can always induce a positive energy flux that can facilitate the barrier-crossing nucleation process. The nonmonotonic feature of the average value of the energy flux with αmay be the origin of our simulation results.

physics.soc-ph↗

arXiv2015

Nucleation of a three-state spin model on complex networks

We study the metastability and nucleation of the Blume-Capel model on complex networks, in which each node can take one of three possible spin variables $\left\{ {-1, 0, 1} \right\}$. We consider the external magnetic field $h$ to be positive, and let the chemical potential $λ$ vary between $-h$ and $h$ in a low temperature, such that the $1$ configuration is stable, and $-1$ configuration and/or $0$ configuration are metastable. Combining the heterogeneous mean-field theory with simulations, we show that there exist four regions with distinct nucleation scenarios depending on the values of $h$ and $λ$: the system undergoes a two-step nucleation process from $-1$ configuration to $0$ configuration and then to $1$ configuration (region I); nucleation becomes a one-step process without an intermediate metastable configuration directly from $-1$ configuration to $1$ configuration (region II(1)) or directly from $0$ configuration to $1$ configuration (region II(2)) depending on the sign of $λ$; the metastability of the system vanishes and nucleation is thus irrelevant (region III). Furthermore, we show that in the region I nucleation rates for each step intersect that results in the occurrence of a maximum in the total nucleation rate.

cond-mat.stat-mech↗

arXiv2014

Complex activated transition in a system of two coupled bistable oscillators

We study the fluctuation-activated transition process in a system of two coupled bistable oscillators, in which each oscillator is driven by one constant force and an independent Gaussian white noise. The transition pathway has been identified and the transition rate has been computed as the coupling strength $μ$ and the mismatch $σ$ in the force constants are varied. For identical oscillators ($σ=0$), the transition undergoes a change from a two-step process with two candidate pathways to a one-step process with also two candidate pathways to a one-step process with a single pathway as $μ$ is increased. For nonidentical oscillators ($σ\neq0$), a novel transition emerges that is a mixture of a two-step pathway and a one-step pathway. Interestingly, we find that the total transition rate depends nonmonotonically on $μ$: a maximal rate appears in an intermediate magnitude of $μ$. Moreover, in the presence of weak coupling the rate also exhibits an unexpected maximum as a function of $σ$. The results are in an excellent agreement with our numerical simulations by forward flux sampling.

cond-mat.stat-mech↗

arXiv2013

Coarse-graining the calcium dynamics on a stochastic reaction-diffusion lattice model

We develop a coarse grained (CG) approach for efficiently simulating calcium dynamics in the endoplasmic reticulum membrane based on a fine stochastic lattice gas model. By grouping neighboring microscopic sites together into CG cells and deriving CG reaction rates using local mean field approximation, we perform CG kinetic Monte Carlo (kMC) simulations and find the results of CG-kMC simulations are in excellent agreement with that of the microscopic ones. Strikingly, there is an appropriate range of coarse proportion $m$, corresponding to the minimal deviation of the phase transition point compared to the microscopic one. For fixed $m$, the critical point increases monotonously as the system size increases, especially, there exists scaling law between the deviations of the phase transition point and the system size. Moreover, the CG approach provides significantly faster Monte Carlo simulations which are easy to implement and are directly related to the microscopics, so that one can study the system size effects at the cost of reasonable computational time.

physics.chem-ph↗

arXiv2013

Mobility-enhanced signal response in metapopulation networks of coupled oscillators

We investigate the effect of mobility on the response of coupled oscillators to a subthreshold external signal in metapopulation networks, wherein each node represents a subpopulation with overdamped bistable oscillators that can randomly diffuse between nodes. With increasing mobility rate, the oscillators undergo transitions from intrawell to interwell motion, demonstrating clearly mobility-enhanced signal amplification. Moreover, the response shows nonmonotonic dependence on the mobility rate, i.e., a maximal gain occurs at a moderate level of mobility. This interesting phenomenon is robust against variations in the overall density, network size, as well as network topology. In addition, a simple mean-field analysis is carried out to qualitatively illustrate the simulation results.

physics.soc-ph↗

arXiv2013

Explosive synchronization transitions in complex neural network

It has been recently reported that explosive synchronization transitions can take place in networks of phase oscillators [Gómez-Gardeñes \emph{et al.} Phys.Rev.Letts. 106, 128701 (2011)] and chaotic oscillators [Leyva \emph{et al.} Phys.Rev.Letts. 108, 168702 (2012)]. Here, we investigate the effect of a microscopic correlation between the dynamics and the interacting topology of coupled FitzHugh-Nagumo oscillators on phase synchronization transition in Barabási-Albert (BA) scale-free networks and Erdös-Rényi (ER) random networks. We show that, if the width of distribution of natural frequencies of the oscillations is larger than a threshold value, a strong hysteresis loop arises in the synchronization diagram of BA networks due to the positive correlation between node degrees and natural frequencies of the oscillations, indicating the evidence of an explosive transition towards synchronization of relaxation oscillators system. In contrast to the results in BA networks, in more homogeneous ER networks the synchronization transition is always of continuous type regardless of the the width of the frequency distribution. Moreover, we consider the effect of degree-mixing patterns on the nature of the synchronization transition, and find that the degree assortativity is unfavorable for the occurrence of such an explosive transition.

cond-mat.dis-nn↗