Scale-Free Networks with Different Types of Nodes

Author(s):  
Juan Zhang ◽  
Wenfeng Wu
2015 ◽  
Vol 26 (06) ◽  
pp. 1550067 ◽  
Author(s):  
Chao Gao ◽  
Lu Zhong ◽  
Xianghua Li ◽  
Zili Zhang ◽  
Ning Shi

Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.


2004 ◽  
Vol 15 (08) ◽  
pp. 1075-1086 ◽  
Author(s):  
SUBHASIS BANERJEE ◽  
SHRESTHA BASU MALLICK ◽  
INDRANI BOSE

We study the discrete Gierer–Meinhardt model of reaction–diffusion on three different types of networks: regular, random and scale-free. The model dynamics lead to the formation of stationary Turing patterns in the steady state in certain parameter regions. Some general features of the patterns are studied through numerical simulation. The results for the random and scale-free networks show a marked difference from those in the case of the regular network. The difference may be ascribed to the small world character of the first two types of networks.


2019 ◽  
Vol 286 (1895) ◽  
pp. 20181949 ◽  
Author(s):  
Xiaojie Chen ◽  
Åke Brännström ◽  
Ulf Dieckmann

Dispersal is a key process for the emergence of social and biological behaviours. Yet, little attention has been paid to dispersal's effects on the evolution of cooperative behaviour in structured populations. To address this issue, we propose two new dispersal modes, parent-preferred and offspring-preferred dispersal, incorporate them into the birth–death update rule, and consider the resultant strategy evolution in the prisoner's dilemma on random-regular, small-world, and scale-free networks, respectively. We find that parent-preferred dispersal favours the evolution of cooperation in these different types of population structures, while offspring-preferred dispersal inhibits the evolution of cooperation in homogeneous populations. On scale-free networks when the strength of parent-preferred dispersal is weak, cooperation can be enhanced at intermediate strengths of offspring-preferred dispersal, and cooperators can coexist with defectors at high strengths of offspring-preferred dispersal. Moreover, our theoretical analysis based on the pair-approximation method corroborates the evolutionary outcomes on random-regular networks. We also incorporate the two new dispersal modes into three other update rules (death-birth, imitation, and pairwise comparison updating), and find that similar results about the effects of parent-preferred and offspring-preferred dispersal can again be observed in the aforementioned different types of population structures. Our work, thus, unveils robust effects of preferential dispersal modes on the evolution of cooperation in different interactive environments.


2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yu Kong ◽  
Tao Li ◽  
Yuanmei Wang ◽  
Xinming Cheng ◽  
He Wang ◽  
...  

AbstractNowadays, online gambling has a great negative impact on the society. In order to study the effect of people’s psychological factors, anti-gambling policy, and social network topology on online gambling dynamics, a new SHGD (susceptible–hesitator–gambler–disclaimer) online gambling spreading model is proposed on scale-free networks. The spreading dynamics of online gambling is studied. The basic reproductive number $R_{0}$ R 0 is got and analyzed. The basic reproductive number $R_{0}$ R 0 is related to anti-gambling policy and the network topology. Then, gambling-free equilibrium $E_{0}$ E 0 and gambling-prevailing equilibrium $E_{ +} $ E + are obtained. The global stability of $E_{0}$ E 0 is analyzed. The global attractivity of $E_{ +} $ E + and the persistence of online gambling phenomenon are studied. Finally, the theoretical results are verified by some simulations.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jinlong Ma ◽  
Junfeng Zhang ◽  
Yongqiang Zhang

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