scholarly journals Dynamically Weighted Clique Evolution Model in Clique Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Zhang-Wei Li ◽  
Xu-Hua Yang ◽  
Feng-Ling Jiang ◽  
Guang Chen ◽  
Guo-Qing Weng ◽  
...  
Keyword(s):  
Real World ◽  
Mean Field Theory ◽  
Mean Field ◽  
Strength Distribution ◽  
Evolution Model ◽  
Edge Weight ◽  
Time Step ◽  
Complete Subgraph ◽  
Scale Free ◽  
The Mean

This paper proposes a weighted clique evolution model based on clique (maximal complete subgraph) growth and edge-weight driven for complex networks. The model simulates the scheme of real-world networks that the evolution of networks is likely to be driven by the flow, such as traffic or information flow needs, as well as considers that real-world networks commonly consist of communities. At each time step of a network’s evolution progress, an edge is randomly selected according to a preferential scheme. Then a new clique which contains the edge is added into the network while the weight of the edge is adjusted to simulate the flow change brought by the new clique addition. We give the theoretical analysis based on the mean field theory, as well as some numerical simulation for this model. The result shows that the model can generate networks with scale-free distributions, such as edge weight distribution and node strength distribution, which can be found in many real-world networks. It indicates that the evolution rule of the model may attribute to the formation of real-world networks.

scholarly journals Associated Credit Risk Contagion with Incubatory Period: A Network-Based Perspective

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kai Xu ◽  
Jianming Mo ◽  
Qian Qian ◽  
Fengying Zhang ◽  
Xiaofeng Xie ◽  
...  
Keyword(s):  
Field Theory ◽  
Credit Risk ◽  
Mean Field Theory ◽  
Mean Field ◽  
Stable State ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Mean ◽  
Credit Risk Contagion

Associated credit risk is a kind of credit risk among the associated credit entities formed by credit-related entities. Focusing on this hot topic of associated credit risk and the relevant contagion and considering the latent entities and their incubatory period, this paper builds an infectious dynamic model to describe the associated credit risk contagion of associated credit entities based on the mean-field theory of complex networks. Firstly, this paper analyzes the stable state of the associated credit risk contagion in the associated entity network, considering the latent entities and their incubatory period. Secondly, from the perspective of complex network and considering the incubatory period, a SHIS model is built to reveal how the incubatory period influences associated credit risk contagion. Finally, the sensitivity of some parameters is analyzed in the Barabási–Albert (BA) scale-free network. The results show the following: (i) the contagion threshold of associated credit risk is related to the incubatory period of latent entities, the recovery rate and infectivity of infected entities, and the newborn rate of credit entities; (ii) the infectious rate of infected entities, the mortality rate of credit entities, and the important factors stated in (i) are all significantly correlated with the density of infected entities.

Research on SIQRS Spreading Dynamic Model with Feedback Mechanism on Scale-Free Networks

2014 ◽  
Vol 989-994 ◽  
pp. 4524-4527
Author(s):  
Tao Li ◽  
Yuan Mei Wang ◽  
You Ping Yang
Keyword(s):  
Dynamic Model ◽  
Mean Field Theory ◽  
Mean Field ◽  
Feedback Mechanism ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Spreading Dynamics ◽  
Scale Free Networks ◽  
The Mean ◽  
The Relationship

A modified spreading dynamic model with feedback-mechanism based on scale-free networks is presented in this study. Using the mean field theory, the spreading dynamics of the model is analyzed. The spreading threshold and equilibriums are derived. The relationship between the spreading threshold, the epidemic steady-state and the feedback-mechanism is analyzed in detail. Theoretical results indicate the feedback-mechanism can increase the spreading threshold, resulting in effectively controlling the epidemic spreading.

Spreading of Epidemics on Scale-Free Networks with Time Delay

2012 ◽  
Vol 562-564 ◽  
pp. 1386-1389
Author(s):  
Yuan Mei Wang ◽  
Tao Li
Keyword(s):  
Field Theory ◽  
Time Delay ◽  
Mean Field Theory ◽  
Mean Field ◽  
Sir Model ◽  
Scale Free ◽  
Spreading Dynamics ◽  
Scale Free Networks ◽  
The Mean ◽  
Theoretical Results

In the SIR model once a node is cured after infection it becomes permanently immune,but we assume this immunity to be temporary. So we obtain an epidemic model with time delay on scale-free networks. Using the mean field theory the spreading threshold and the spreading dynamics is analyzed. Theoretical results indicate that the threshold is significantly dependent on the topology of scale-free networks and time delay. Numerical simulations confirmed the theoretical results.

Topology properties of a weighted multi-local-world evolving network

2015 ◽  
Vol 93 (3) ◽  
pp. 353-360 ◽  
Author(s):  
Meifeng Dai ◽  
Danping Zhang ◽  
Lei Li
Keyword(s):  
Real World ◽  
Weight Distribution ◽  
Preferential Attachment ◽  
Mean Field ◽  
Field Method ◽  
Strength Distribution ◽  
Power Law Distribution ◽  
World Model ◽  
The Mean ◽  
Theoretical Results

Many real-world networks, ranging from the world trade web to the Internet network, have been described by multi-local-worlds. It is obvious that the nodes within a local world are much more connected to each other than to the others outside the local world. A multi-local-world model can capture and describe these real-world networks’ topological properties. Based on the local-world model, a weighted multi-local-world evolving network model is presented. This model combines selected nodes with preferential attachment and three kinds of local changes of weights. Using a rate equation and the mean-field method, we study the network’s properties: the weight distribution and the strength distribution. We theoretically prove that the weight distribution and the strength distribution follow a power-law distribution in some conditions. Numerical simulations are in agreement with the theoretical results.

Spreading Dynamics Analysis of a Novel SEAIRS Epidemic Model with Protection and Hospital Quarantine under Government Pre-warning

2021 ◽  
Author(s):  
Bingchuan Xue ◽  
Tao Li ◽  
Xinming Cheng ◽  
Yumiao Li ◽  
Yuanyuan Wu ◽  
...  
Keyword(s):  
Epidemic Model ◽  
Mean Field Theory ◽  
Mean Field ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Spreading Dynamics ◽  
The Mean ◽  
The Impact

Abstract To study the impact of protection and hospital quarantine measure, government pre-warning mechanism and heterogeneity of underlying networks on epidemic spreading, a novel SEAIRS epidemic model is proposed on scale-free networks. The spreading dynamics of the model is studied by means of the mean-field theory. Two equilibriums and the basic reproductive number R0 of the model is analyzed in detail. The global asymptotic stability of the disease-free equilibrium, the permanence of the epidemic spreading and the global attractivity of the endemic equilibrium are proved. Sensitivity analysis shows that the basic reproductive number R0 is dependent on the coverage rate of home quarantine (ωQ,ηA ,ηS ), hospitalization rate η1 and government pre-warning intensity δ . Finally, the theoretical analysis results are confirmed by means of numerical simulations.

Comparative empirical analysis of flow-weighted transit route networks in R-space and evolution modeling

2017 ◽  
Vol 31 (12) ◽  
pp. 1750087 ◽  
Author(s):  
Ailing Huang ◽  
Guangzhi Zang ◽  
Zhengbing He ◽  
Wei Guan
Keyword(s):  
Empirical Analysis ◽  
Mean Field Theory ◽  
Mean Field ◽  
Dynamical Evolution ◽  
Mixed Complex ◽  
Design Strategies ◽  
Time Step ◽  
Transit System ◽  
Scale Free ◽  
Transit Route

Urban public transit system is a typical mixed complex network with dynamic flow, and its evolution should be a process coupling topological structure with flow dynamics, which has received little attention. This paper presents the R-space to make a comparative empirical analysis on Beijing’s flow-weighted transit route network (TRN) and we found that both the Beijing’s TRNs in the year of 2011 and 2015 exhibit the scale-free properties. As such, we propose an evolution model driven by flow to simulate the development of TRNs with consideration of the passengers’ dynamical behaviors triggered by topological change. The model simulates that the evolution of TRN is an iterative process. At each time step, a certain number of new routes are generated driven by travel demands, which leads to dynamical evolution of new routes’ flow and triggers perturbation in nearby routes that will further impact the next round of opening new routes. We present the theoretical analysis based on the mean-field theory, as well as the numerical simulation for this model. The results obtained agree well with our empirical analysis results, which indicate that our model can simulate the TRN evolution with scale-free properties for distributions of node’s strength and degree. The purpose of this paper is to illustrate the global evolutional mechanism of transit network that will be used to exploit planning and design strategies for real TRNs.

Epidemic Spreading with Feedback Mechanism and Time Delay under Migration in Scale-Free Networks

2013 ◽  
Vol 378 ◽  
pp. 655-661
Author(s):  
Tao Li ◽  
Yuan Mei Wang
Keyword(s):  
Time Delay ◽  
Mean Field Theory ◽  
Mean Field ◽  
Feedback Mechanism ◽  
Critical Threshold ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Epidemic Dynamics ◽  
Scale Free Networks ◽  
The Mean

Taking into account the heterogeneity of the underlying networks, an epidemic model with feedback-mechanism, time delay and migrations of individuals on scale-free networks is presented. First, the epidemic dynamics is analyzed via the mean field theory. The spreading critical threshold and equilibriums are derived. The existence of endemic equilibrium is determined by the spreading threshold. Then, the influences of feedback-mechanism, time delay, migrations of individuals and the heterogeneity of the scale-free networks on the spreading threshold and the epidemic steady-state are studied in detail. Numerical simulations are presented to illustrate the results with the theoretical analysis.

EPIDEMICS OF SIRS MODEL WITH NONUNIFORM TRANSMISSION ON SCALE-FREE NETWORKS

2009 ◽  
Vol 23 (09) ◽  
pp. 2203-2213 ◽  
Author(s):  
C. Y. XIA ◽  
S. W. SUN ◽  
Z. X. LIU ◽  
Z. Q. CHEN ◽  
Z. Z. YUAN
Keyword(s):  
Disease Transmission ◽  
Mean Field Theory ◽  
Mean Field ◽  
Critical Threshold ◽  
Epidemic Threshold ◽  
Scale Free ◽  
Sirs Epidemic Model ◽  
Sirs Model ◽  
Scale Free Networks ◽  
The Mean

We investigate the effect of nonuniform transmission on the critical threshold of susceptible–infected–recovered–susceptible (SIRS) epidemic model on scale-free networks. Based on the mean-field theory, it is observed that the epidemic threshold is not only correlated with the topology of underlying networks, but also with the disease transmission mechanism (e.g., nonuniform transmission). The current findings will significantly help us to further understand the real epidemics taking place on social and technological networks.

Sign in / Sign up

Export Citation Format

Share Document