Degree distribution of random birth-and-death network with network size decline

Zhang, Xiaojun; Yang, Huilan

doi:10.1088/1674-1056/25/6/060202

Cited by 8 publications

(4 citation statements)

References 33 publications

(50 reference statements)

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“…In contrast, the degree distribution of BA scale-free network approximately follows the power-law distribution, and the maximum and minimum values of degree have a significant disparity. [50] Identically, the difference of degree is quite distinct in Facebook network and there are more hub nodes than BA network. To some extent, WS network can represent a real-world network which has a small diameter.…”

Section: Simulation Resultsmentioning

confidence: 99%

Correlation and trust mechanism-based rumor propagation model in complex social networks

Sun¹,

Wang²,

Cang³

2022

Chinese Phys. B

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In real life, the process of rumor propagation is influenced by many factors. The complexity and uncertainty of human psychology make the diffusion model more challenging to depict. In order to establish a comprehensive propagation model, in this paper, we take some psychological factors into consideration to mirror rumor propagation. Firstly, we use the Ridenour model to combine the trust mechanism with the correlation mechanism and propose a modified rumor propagation model. Secondly, mean-field equations which describe the dynamics of the modified SIR model on homogenous and heterogeneous networks are derived. Thirdly, a steady-state analysis is conducted for the spreading threshold and the final rumor size. Fourthly, we investigate rumor immunization strategies and obtain immunization thresholds. Next, simulations on different networks are carried out to verify the theoretical results and the effectiveness of the immunization strategies. The results indicate that the utilization of trust and correlation mechanisms leads to a larger final rumor size and a smaller terminal time. Moreover, different immunization strategies have disparate effectiveness in rumor propagation.

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Section: Simulation Resultsmentioning

confidence: 99%

Correlation and trust mechanism-based rumor propagation model in complex social networks

Sun¹,

Wang²,

Cang³

2022

Chinese Phys. B

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“…Later, a reversible Markov chain formulation was proposed to obtain the stationary distribution of degrees [19]. By the Markov chain, an exact formula of degree distributions was obtained in a random evolving network [20]. Apart from degree distributions, many other topological properties were also studied by researchers, e.g., betweenness [21], clustering coefficient [22], and assortativity [23] of networks.…”

Section: Related Workmentioning

confidence: 99%

Evolving Network Modeling Driven by the Degree Increase and Decrease Mechanism

Feng

Kurths

2023

IEEE Trans. Syst. Man Cybern, Syst.

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Ever since the Barabási-Albert (BA) scale-free network has been proposed, network modeling has been studied intensively in light of the network growth and the preferential attachment (PA). However, numerous real systems are featured with a dynamic evolution including network reduction in addition to network growth. In this paper, we propose a novel mechanism for evolving networks from the perspective of vertex degree. We construct a queueing system to describe the increase and decrease of vertex degree, which drives the network evolution. In our mechanism, the degree increase rate is regarded as a function positively correlated to the degree of a vertex, ensuring the preferential attachment in a new way. Degree distributions are investigated under two expressions of the degree increase rate, one of which manifests a "long tail", and another one varies with different values of parameters. In simulations, we compare our theoretical distributions with simulation results and also apply them to real networks, which presents the validity and applicability of our model.

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“…5 and Table 1, the degree distribution of BA scale-free network nodes approximately follows the power-law distribution, which reflects the scale-free characteristic, and the maximum and minimum degrees have a large gap. [37] However, the average degree and clustering coefficient are small. All of these reflect the characteristics of a small world, which reflect characteristics similar to real social networks.…”

Section: P Kmentioning

confidence: 99%

Direct immune-SCIR public-opinion propagation model based on real-time online users*

Wang¹,

Guo²,

Li³

et al. 2020

Chinese Phys. B

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Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

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Degree distribution of random birth-and-death network with network size decline

Cited by 8 publications

References 33 publications

Correlation and trust mechanism-based rumor propagation model in complex social networks

Correlation and trust mechanism-based rumor propagation model in complex social networks

Evolving Network Modeling Driven by the Degree Increase and Decrease Mechanism

Direct immune-SCIR public-opinion propagation model based on real-time online users*

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