Article
Computer Science, Interdisciplinary Applications
Linhe Zhu, Wenshan Liu, Zhengdi Zhang
Summary: A novel coupled two-layered networking framework is proposed for modeling epidemic spreading and information spreading. By establishing mathematical equations and conducting numerical simulations, the model can help prevent and control epidemic outbreaks effectively.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2021)
Article
Physics, Multidisciplinary
Jin-Ying Dai, Cong LI, Xiang LI
Summary: This study investigates the interaction between epidemic spread and the diffusion of epidemic-related information using a coupled susceptible-infected-vaccinated-recovered/unaware-aware-unaware (SIRV-UAU) model, considering individual social differences. The results show that increasing social reinforcement strength, primary spreading rate, or the influence factor of uploading information can promote information diffusion and inhibit the spread of the epidemic. The impact of the influence factor of individuals uploading information is found to be significant. Moreover, the reduction factor and social reinforcement strength have an effect on the spread of the epidemic.
Article
Mathematics, Interdisciplinary Applications
Paulo Cesar Ventura, Alberto Aleta, Francisco A. Rodrigues, Yamir Moreno
Summary: This study presents a model for epidemic spreading in temporal networks of mobile agents that incorporates local behavioral responses. It shows that the mechanism of behavioral responses can effectively reduce the spread of disease when the spatial density of agents is low, but it can cause an abrupt phase transition and the emergence of a new bistable phase at higher densities. The study also characterizes the temporal networks formed in the fast mobility regime and examines how the behavioral mechanism affects degree distributions and other metrics.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Mathematics
Bo Song, Huiming Wu, Yurong Song, Xu Wang, Guoping Jiang
Summary: In this paper, a novel weighted co-evolving multiplex network model is proposed to describe the interaction between information diffusion in online social networks and epidemic spreading in adaptive physical contact networks. The simulation results show that the maximum infection scale decreases as the information acceptance probability grows, and the final infection decreases as the rewiring behaviors increase. Interestingly, an infection peak appears in our model due to the interaction between information diffusion and epidemic spread.
Article
Physics, Multidisciplinary
Jiang Wu, Renxian Zuo, Chaocheng He, Hang Xiong, Kang Zhao, Zhongyi Hu
Summary: This study proposes an aware-susceptible-infected model (ASI) to examine the impact of information literacy on the spreading process in multiplex networks using the microscopic Markov chain method. The results show that individuals with high information literacy are more responsive to information adoption. Additionally, the effectiveness of epidemic information in suppressing transmission depends on individuals' abilities to translate awareness into protective behaviors and varies according to community characteristics. This study highlights the importance of individual heterogeneity in information literacy in epidemic spreading within different communities.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Mathematics, Applied
Xiaoyu Xue, WenYao Li, Yanyi Nie, Xun Lei, Tao Lin, Wei Wang
Summary: This study investigates the cooperative spreading of two epidemics in a simplicial complex, including structural and dynamical reinforcement effects. It reveals that increasing the reinforcement effect enhances the spreading dynamics, resulting in a larger outbreak size and smaller threshold.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Jinlong Ma, Tingting Xiang, Yongbin Zhao
Summary: Recent studies have found that real-world systems can be described using multi-layer complex networks. This paper introduces a traffic-driven SIR epidemic spreading model on a logical-physical layered network. The study investigates the features of epidemic spreading on a layered network based on the density of infected and recovered nodes. The results show that traffic flow significantly affects the intensity and scope of epidemic spreading. Comparing the effects of different types of two-layer networks on epidemic spreading, it is found that homogenous logical or physical network structures promote epidemic spread more than heterogeneous networks. This work could contribute to the design of traffic-driven epidemic prevention and control strategies.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2023)
Article
Mathematics
Nickie Lefevr, Andreas Kanavos, Vassilis C. Gerogiannis, Lazaros Iliadis, Panagiotis Pintelas
Summary: Complex networks, derived from the observation and analysis of real-world networks, include biological networks focusing on connections and interfaces like epidemic models. Fuzzy logic, a powerful mathematical tool, deals with imprecision and aims to provide low-cost solutions to real-world problems. Fuzzy-based simulation scenarios for HIV spreading in a population of needle drug users demonstrate the importance of fuzziness in analyzing disease spread.
Article
Mathematics, Applied
Minyu Feng, Xiangxi Li, Yuhan Li, Qin Li
Summary: In this study, a two-layer network-based epidemic spreading model was proposed to investigate the influence of individuals with different properties in the awareness layer on disease transmission. It was found that individuals with high centrality in the awareness layer significantly inhibit the spread of infectious diseases, while individuals with low centrality in the awareness layer have an approximately linear effect on the number of infected individuals.
Article
Physics, Fluids & Plasmas
Shogo Mizutaka, Kizashi Mori, Takehisa Hasegawa
Summary: We investigate the effect of degree correlation on a susceptible-infected-susceptible (SIS) model with a nonlinear cooperative effect. Regardless of synergy, positive and negative degree correlation in the model reduces and raises the epidemic threshold, respectively. For networks with a strongly positive degree correlation, the model predicts the emergence of two discontinuous jumps in the steady-state infected density.
Article
Physics, Applied
Tianqiao Zhang, Ruijie Wang, Yang Zhang, Junliang Chen, Xuzhen Zhu
Summary: The study explores the impact of seeds on cooperative epidemic spreading on complex networks, showing continuous phase transition of node infection proportions on different networks with selection strategy not altering the transition types. Eigenvector centrality promotes cooperative spreading on artificial networks, while degree centrality promotes the spread of two cooperative diseases on real-world networks.
INTERNATIONAL JOURNAL OF MODERN PHYSICS B
(2021)
Article
Mathematics, Applied
Qian Yin, Zhishuang Wang, Chengyi Xia, Chris T. Bauch
Summary: In this paper, a three-layer coupled network model is established to analyze the co-evolution of negative vaccine-related information, vaccination behavior, and epidemic spread. The study reveals that the proportion of vaccinated individuals and the topology of the epidemic spread layer play crucial roles in determining the epidemic threshold. Simulation experiments further investigate the impact of various factors on epidemic spread, including negative vaccine-related information diffusion, rational judgment, herd mentality, and vaccine cost.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Computer Science, Artificial Intelligence
Xiao Hong, Yuexing Han, Gouhei Tanaka, Bing Wang
Summary: This study aims to investigate the effects of dynamic multi-source information and behavioral responses on the co-evolution of epidemic and information in time-varying multiplex networks. The UAU-SIS model with time-varying self-awareness and behavioral responses is proposed, and the analytical epidemic thresholds for the model are derived. Experimental results show that time-varying behavioral responses can effectively suppress the epidemic spread, while time-varying self-awareness only reduces the scale of the spread. The role of dynamical multi-source information in suppressing epidemic spread is limited.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics, Interdisciplinary Applications
Lilei Han, Zhaohua Lin, Qingqing Yin, Ming Tang, Shuguang Guan, Marian Boguna
Summary: This paper proposes a general formalism to study non-Markovian dynamics on non-Markovian temporal networks. The study finds that, under certain conditions, non-Markovian dynamics on temporal networks are equivalent to Markovian dynamics on static networks, independent of the underlying network topology.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Multidisciplinary Sciences
Abu Zobayer, Mohammad Sharif Ullah, K. M. Ariful Kabir
Summary: Evolutionary epidemiological models have been used to analyze contagious diseases and intervention policies in biology. The SVITR epidemic dynamic model is developed by adding compartments for treatment and vaccination. The study considers the rates at which infected individuals enter the treatment and recover state, and uses evolutionary game theory to investigate the rate of change from susceptible to vaccinated and infected to treatment. Numerical simulation suggests that effective vaccination and treatment can reduce the community risk of infection.
SCIENTIFIC REPORTS
(2023)
Article
Mathematics, Applied
Huan Wang, Chuang Ma, Han-Shuang Chen, Hai-Feng Zhang
Summary: The study introduces a new coupled epidemic-awareness model in multiplex networks, considering both asymptomatic state and self-initiated awareness mechanism. Findings suggest that asymptomatic state leads to underestimation of disease risk, resulting in epidemic outbreak, while the self-initiated mechanism plays a positive role in controlling the epidemic.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Physics, Multidisciplinary
Chuansheng Shen, Hanshuang Chen
Summary: This study investigates the impact of non-equilibrium diffusion on nucleation in small-world Ising networks using the forward flux sampling method, revealing that diffusion probability and rewiring probability significantly influence nucleation rate, with different effects depending on the situation.
COMMUNICATIONS IN THEORETICAL PHYSICS
(2021)
Article
Automation & Control Systems
Qi-Ming Liu, Chuang Ma, Bing-Bing Xiang, Han-Shuang Chen, Hai-Feng Zhang
Summary: This article presents a framework for inferring the structures and dynamics of complex networks based on time series data, even when the original dynamics are unknown. The method uses a Logistic regression with Sigmoid function to describe transition probabilities and employs mean-field approximation for maximum likelihood estimation. The framework has been validated on synthetic and empirical networks, showing high accuracy in revealing network structures and estimating dynamical processes.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Mechanics
Hanshuang Chen, Feng Huang, Chuansheng Shen, Guofeng Li, Haifeng Zhang
Summary: In this study, we obtained the probability distribution of the degree assortativity coefficient on configuration networks through simulations, revealing anomalous scaling of the fluctuations in highly heterogeneous networks.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mechanics
Yanfei Ye, Hanshuang Chen
Summary: In this study, we investigate random walks on complex networks with stochastic resetting that is dependent on the nodes. By employing a renewal approach, we derive precise expressions for the stationary occupation probabilities of the walker on each node and the mean first passage time between any two nodes. We validate our theoretical findings by conducting numerical simulations on three networks with two different resetting protocols. We discover that, under appropriate settings, optimizing the efficiency of a global search on such networks can be achieved through node-dependent resetting probability.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Multidisciplinary Sciences
Huan Wang, Chuang Ma, Han-Shuang Chen, Ying-Cheng Lai, Hai-Feng Zhang
Summary: Researchers have developed a general framework that combines statistical inference and expectation maximization to fully reconstruct the topology of 2-simplicial complexes with two- and three-body interactions based on binary time-series data. The framework's effectiveness has been validated, demonstrating its robustness against noisy data or stochastic disturbance.
NATURE COMMUNICATIONS
(2022)
Article
Physics, Multidisciplinary
Hanshuang Chen, Guofeng Li, Feng Huang
Summary: This paper investigates the effect of stochastic resetting on the first passage properties of discrete-time absorbing Markov chains. The authors derive the mean first passage time and splitting probabilities using a renewal approach. They also present a sufficient condition for optimizing the mean first passage time and apply their results to two specific examples.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2022)
Article
Automation & Control Systems
Kezan Li, Mengchen Wang, Qi Yang, Yi Qin, Haifeng Zhang
Summary: In this article, a new nonlinear stochastic network model is proposed to achieve successive lag synchronization. Both constant and adaptive pinning control strategies are designed to regulate the synchronization. The theoretical results are validated through numerical simulations.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Interdisciplinary Applications
Jia-Wei Wang, Hai-Feng Zhang, Xiao-Jing Ma, Jing Wang, Chuang Ma, Pei-Can Zhu
Summary: In this paper, a privacy-preserving framework named HE-ranking is proposed to identify influential nodes in networks based on homomorphic encryption protocol. The method collaboratively computes the nodes' importance and protects the sensitive information of each private network, effectively identifying the influential nodes while preserving privacy.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2023)
Article
Physics, Fluids & Plasmas
Yating Wang, Hanshuang Chen
Summary: In this paper, the authors investigate the effects of stochastic resetting on the entropy rate of discrete-time Markovian processes. The study reveals nontrivial and interesting features of stochastic dynamics, showing a nonmonotonic dependence of the entropy rate on the resetting probability. The research also explores the mixing properties of stochastic processes on different network topologies.
Article
Physics, Fluids & Plasmas
Hanshuang Chen, Yanfei Ye
Summary: This study investigates discrete-time random walks on networks subject to time-dependent stochastic resetting. The results demonstrate that time-modulated resetting protocols can be more advantageous in accelerating the completion of a target search process compared to constant-probability resetting.
Article
Physics, Fluids & Plasmas
Hanshuang Chen, Feng Huang
Summary: The study investigates the first passage properties of a Brownian particle diffusing freely inside a d-dimensional sphere with absorbing spherical surface subject to stochastic resetting. It finds the existence of a nonzero optimal resetting rate for minimum mean time to absorption, and observes different transition behaviors under varying conditions. The research also explores the effects of resetting on the mean time to absorption when the particle is located between two-dimensional or three-dimensional concentric spheres with absorbing boundaries.
Article
Mathematics, Applied
Shuang Wang, Hanshuang Chen, Feng Huang
Summary: This paper investigates discrete-time random walks on complex networks with multiple resetting nodes, deriving expressions for the occupation probability and mean first-passage time. The advantage of resetting processes to multiple resetting nodes in global search is demonstrated. The distribution of resetting probabilities is optimized to minimize the mean first-passage time averaged over distinct nodes.
Article
Physics, Multidisciplinary
Ma Chuang, Yang Xiao-Long, Chen Han-Shuang, Zhang Hai-Feng
Summary: The study introduces a mean-field approximation method to modify the joint probability distribution in order to make the BP algorithm match perfectly, making theoretical derivation easier to understand.
ACTA PHYSICA SINICA
(2021)
Article
Physics, Fluids & Plasmas
Feng Huang, Hanshuang Chen
Summary: This study investigates discrete-time random walks with first-passage resetting processes on arbitrary networks, deriving exact expressions for stationary occupation probability, average number of resets, and mean first-passage time. Results show that these quantities can be expressed in terms of the fundamental matrix, demonstrating the advantage of first-passage resetting in global search on various networks.
