arXiv subjects

Han-Xin Yang

Publications and source records attributed to Han-Xin Yang.

At least 19 recordsLinked to original sources

arXiv2022

Game-theoretical approach for opinion dynamics on social networks

Opinion dynamics on social networks have been received considerable attentions in recent years. Nevertheless, just a few works have theoretically analyzed the condition in which a certain opinion can spread in the whole structured population. In this paper, we propose an evolutionary game approach for a binary opinion model to explore the conditions for an opinion's spreading. Inspired by real-life observations, we assume that an agent's choice to select an opinion is not random, but is based on a score rooted both from public knowledge and the interactions with neighbors. By means of coalescing random walks, we obtain a condition in which opinion $A$ can be favored to spread on social networks in the weak selection limit. We find that the successfully spreading condition of opinion $A$ is closely related to the basic scores of binary opinions, the feedback scores on opinion interactions, and the structural parameters including the edge weights, the weighted degrees of vertices, and the average degree of the network. In particular, when individuals adjust their opinions based solely on the public information, the vitality of opinion $A$ depends exclusively on the difference of basic scores of $A$ and $B$. When there are no negative (positive) feedback interactions between connected individuals, we find that the success of opinion $A$ depends on the ratio of the obtained positive (negative) feedback scores of competing opinions. To complete our study, we perform computer simulations on fully-connected, small-world, and scale-free networks, respectively, which support and confirm our theoretical findings.

cs.GT↗

arXiv2018

Kuramoto dilemma alleviated by optimizing connectivity and rationality

Recently, Antonioni and Cardillo proposed a coevolutionary model based on the intertwining of oscillator synchronization and evolutionary game theory [Phys. Rev. Lett. \textbf{118}, 238301 (2017)], in which each Kuramoto oscillator can decide whether to interact-or not-with its neighbors, and all oscillators can receive some benefits from the local synchronization but those who choose to interact must pay a cost. Oscillators are allowed to update their strategies according to payoff difference, wherein the strategy of an oscillator who has obtained higher payoff is more likely to be followed. Utilizing this coevolutionary model, we find that the global synchronization level reaches the highest level when the average degree of the underlying interaction network is moderate. We also study how synchronization is affected by the individual rationality in choosing strategy.

physics.soc-ph↗

arXiv2018

Enhancement of cooperation by giving high-degree neighbors more help

In this paper, we study the effect of preferential assistance on cooperation in the donation game. Cooperators provide benefits to their neighbors at some costs. Defectors pay no cost and do not distribute any benefits. The total contribution of a cooperator is fixed and he/she distributes his/her contribution unevenly to his/her neighbors. Each individual is assigned a weight that is the power of its degree, where the exponent $α$ is an adjustable parameter. The amount that cooperator $i$ contributes to a neighbor $j$ is proportional to $j$'s weight. Interestingly, we find that there exists an optimal value of $α$ (which is positive), leading to the highest cooperation level. This phenomenon indicates that, to enhance cooperation, individuals could give high-degree neighbors more help, but only to a certain extent.

physics.soc-ph↗

arXiv2018

Promoting cooperation by reputation-driven group formation

In previous studies of spatial public goods game, each player is able to establish a group. However, in real life, some players cannot successfully organize groups for various reasons. In this paper, we propose a mechanism of reputation-driven group formation, in which groups can only be organized by players whose reputation reaches or exceeds a threshold. We define a player's reputation as the frequency of cooperation in the last $T$ time steps. We find that the highest cooperation level can be obtained when groups are only established by pure cooperators who always cooperate in the last $T$ time steps. Effects of the memory length $T$ on cooperation are also studied.

physics.soc-ph↗

arXiv2018

Promoting cooperation by punishing minority

Punishment is an effective way to sustain cooperation among selfish individuals. In most of previous studies, objects of punishment are set to be defectors. In this paper, we propose a mechanism of punishment, in which individuals with the majority strategy will punish those with the minority strategy in a public goods game group. Both theoretical analysis and simulation show that the cooperation level can be greatly enhanced by punishing minority. For no punishment or small values of punishment fine, the fraction of cooperators continuously increases with the multiplication factor. However, for large values of punishment fine, there exists a critical value of multiplication factor, at which the fraction of cooperators suddenly jumps from 0 to 1. The density of different types of groups is also studied.

physics.soc-ph↗

arXiv2018

Enhancement of cooperation through conformity-driven reproductive ability

We propose a conformity-driven reproductive ability in which an individual $i$ is more (less) likely to imitate a neighbor $j$'s strategy if $j$'s strategy is majority (minority) in $i$'s neighborhood. The results on the evolutionary spatial prisoner's dilemma game show that, compared to homogeneous reproductive ability, conformity-driven reproductive ability can greatly enhance cooperation. This finding is robust with respect to different types of network structures (including square lattice and scale-free network) and to different ways of strategy updating (including synchronous and asynchronous strategy updating).

physics.soc-ph↗

arXiv2018

Immunization of traffic-driven epidemic spreading

In this paper, we study the control of the traffic-driven epidemic spreading by immunization strategy. We consider the random, degree-based and betweeness-based immunization strategies, respectively. It is found that the betweeness-based immunization strategy can most effectively prevent the outbreak of traffic-driven epidemic. Besides, we find that the critical number of immune nodes above which epidemic dies out is increased with the enhancement of the spreading rate and the packet-generation rate.

physics.soc-ph↗

arXiv2018

Suppressing epidemic spreading by risk-averse migration in dynamical networks

In this paper, we study the interplay between individual behaviors and epidemic spreading in a dynamical network. We distribute agents on a square-shaped region with periodic boundary conditions. Every agent is regarded as a node of the network and a wireless link is established between two agents if their geographical distance is less than a certain radius. At each time, every agent assesses the epidemic situation and make decisions on whether it should stay in or leave its current place. An agent will leave its current place with a speed if the number of infected neighbors reaches or exceeds a critical value $E$. Owing to the movement of agents, the network's structure is dynamical. Interestingly, we find that there exists an optimal value of $E$ leading to the maximum epidemic threshold. This means that epidemic spreading can be effectively controlled by risk-averse migration. Besides, we find that the epidemic threshold increases as the recovering rate increases, decreases as the contact radius increases, and is maximized by an optimal moving speed. Our findings offer a deeper understanding of epidemic spreading in dynamical networks.

physics.soc-ph↗

arXiv2018

Traffic-driven epidemic spreading on scale-free networks with tunable degree distribution

We study the traffic-driven epidemic spreading on scale-free networks with tunable degree distribution. The heterogeneity of networks is controlled by the exponent $γ$ of power-law degree distribution. It is found that the epidemic threshold is minimized at about $γ=2.2$. Moreover, we find that nodes with larger algorithmic betweenness are more likely to be infected. We expect our work to provide new insights into the effect of network structures on traffic-driven epidemic spreading.

physics.soc-ph↗

arXiv2018

A consensus opinion model based on the evolutionary game

We propose a consensus opinion model based on the evolutionary game. In our model, both of the two connected agents receive a benefit if they have the same opinion, otherwise they both pay a cost. Agents update their opinions by comparing payoffs with neighbors. The opinion of an agent with higher payoff is more likely to be imitated. We apply this model in scale-free networks with tunable degree distribution. Interestingly, we find that there exists an optimal ratio of cost to benefit, leading to the shortest consensus time. Qualitative analysis is obtained by examining the evolution of the opinion clusters. Moreover, we find that the consensus time decreases as the average degree of the network increases, but increases with the noise introduced to permit irrational choices. The dependence of the consensus time on the network size is found to be a power-law form. For small or larger ratio of cost to benefit, the consensus time decreases as the degree exponent increases. However, for moderate ratio of cost to benefit, the consensus time increases with the degree exponent. Our results may provide new insights into opinion dynamics driven by the evolutionary game theory.

physics.soc-ph↗

arXiv2015

Peer pressure: enhancement of cooperation through mutual punishment

An open problem in evolutionary game dynamics is to understand the effect of peer pressure on cooperation in a quantitative manner. Peer pressure can be modeled by punishment, which has been proved to be an effective mechanism to sustain cooperation among selfish individuals. We investigate a symmetric punishment strategy, in which an individual will punish each neighbor if their strategies are different, and vice versa. Because of the symmetry in imposing the punishment, one might expect intuitively the strategy to have little effect on cooperation. Utilizing the prisoner's dilemma game as a prototypical model of interactions at the individual level, we find, through simulation and theoretical analysis, that proper punishment, when even symmetrically imposed on individuals, can enhance cooperation. Besides, we find that the initial density of cooperators plays an important role in the evolution of cooperation driven by mutual punishment.

physics.soc-ph↗

arXiv2015

Traffic-driven epidemic spreading in correlated networks

In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on {\em correlated} networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a non-monotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling/harnessing real-world epidemic spreading dynamics driven by traffic flows.

physics.soc-ph↗

arXiv2015

Opinion percolation in structured population

In a recent work [Shao $et$ $al$ 2009 Phys. Rev. Lett. \textbf{108} 018701], a nonconsensus opinion (NCO) model was proposed, where two opinions can stably coexist by forming clusters of agents holding the same opinion. The NCO model on lattices and several complex networks displays a phase transition behavior, which is characterized by a large spanning cluster of nodes holding the same opinion appears when the initial fraction of nodes holding this opinion is above a certain critical value. In the NCO model, each agent will convert to its opposite opinion if there are more than half of agents holding the opposite opinion in its neighborhood. In this paper, we generalize the NCO model by assuming that each agent will change its opinion if the fraction of agents holding the opposite opinion in its neighborhood exceeds a threshold $T$ ($T\geq 0.5$). We call this generalized model as the NCOT model. We apply the NCOT model on different network structures and study the formation of opinion clusters. We find that the NCOT model on lattices displays a continuous phase transition. For random graphs and scale-free networks, the NCOT model shows a discontinuous phase transition when the threshold is small and the average degree of the network is large, while in other cases the NCOT model displays a continuous phase transition.

physics.soc-ph↗

arXiv2015

Suppressing traffic-driven epidemic spreading by use of the efficient routing protocol

Despite extensive work on the interplay between traffic dynamics and epidemic spreading, the control of epidemic spreading by routing strategies has not received adequate attention. In this paper, we study the impact of efficient routing protocol on epidemic spreading. In the case of infinite node-delivery capacity, where the traffic is free of congestion, we find that that there exists optimal values of routing parameter, leading to the maximal epidemic threshold. This means that epidemic spreading can be effectively controlled by fine tuning the routing scheme. Moreover, we find that an increase in the average network connectivity and the emergence of traffic congestion can suppress the epidemic outbreak.

physics.soc-ph↗

arXiv2015

Cooperation percolation in spatial prisoner's dilemma game

The paradox of cooperation among selfish individuals still puzzles scientific communities. Although a large amount of evidence has demonstrated that cooperator clusters in spatial games are effective to protect cooperators against the invasion of defectors, we continue to lack the condition for the formation of a giant cooperator cluster that assures the prevalence of cooperation in a system. Here, we study the dynamical organization of cooperator clusters in spatial prisoner's dilemma game to offer the condition for the dominance of cooperation, finding that a phase transition characterized by the emergence of a large spanning cooperator cluster occurs when the initial fraction of cooperators exceeds a certain threshold. Interestingly, the phase transition belongs to different universality classes of percolation determined by the temptation to defect $b$. Specifically, on square lattices, $1<b<4/3$ leads to a phase transition pertaining to the class of regular site percolation, whereas $3/2<b<2$ gives rise to a phase transition subject to invasion percolation with trapping. Our findings offer deeper understanding of the cooperative behaviors in nature and society.

physics.soc-ph↗

arXiv2015

Disassortative mixing accelerates consensus in the naming game

In this paper, we study the role of degree mixing in the naming game. It is found that consensus can be accelerated on disassortative networks. We provide a qualitative explanation of this phenomenon based on clusters statistics. Compared with assortative mixing, disassortative mixing can promote the merging of different clusters, thus resulting in a shorter convergence time. Other quantities, including the evolutions of the success rate, the number of total words and the number of different words, are also studied.

physics.soc-ph↗

arXiv2015

Suppressing traffic-driven epidemic spreading by edge-removal strategies

The interplay between traffic dynamics and epidemic spreading on complex networks has received increasing attention in recent years. However, the control of traffic-driven epidemic spreading remains to be a challenging problem. In this Brief Report, we propose a method to suppress traffic-driven epidemic outbreak by properly removing some edges in a network. We find that the epidemic threshold can be enhanced by the targeted cutting of links among large-degree nodes or edges with the largest algorithmic betweeness. In contrast, the epidemic threshold will be reduced by the random edge removal. These findings are robust with respect to traffic-flow conditions, network structures and routing strategies. Moreover, we find that the shutdown of targeted edges can effectively release traffic load passing through large-degree nodes, rendering a relatively low probability of infection to these nodes.

physics.soc-ph↗

arXiv2015

Promoting collective motion of self-propelled agents by distance-based influence

We propose a dynamic model for a system consisting of self-propelled agents in which the influence of an agent on another agent is weighted by geographical distance. A parameter $α$ is introduced to adjust the influence: the smaller value of $α$ means that the closer neighbors have stronger influence on the moving direction. We find that there exists an optimal value of $α$, leading to the highest degree of direction consensus. The value of optimal $α$ increases as the system size increases, while it decreases as the absolute velocity, the sensing radius and the noise amplitude increase.

physics.soc-ph↗