\`x^2+y_1+z_12^34\`
Advanced Search
Article Contents
Article Contents

Structural calculations and propagation modeling of growing networks based on continuous degree

  • * Corresponding authorr: Zhen Jin

    * Corresponding authorr: Zhen Jin 
This work is supported by the National Natural Science Foundation of China (11331009,11171314).
Abstract / Introduction Full Text(HTML) Figure(8) / Table(1) Related Papers Cited by
  • When a network reaches a certain size, its node degree can be considered as a continuous variable, which we will call continuous degree. Using continuous degree method (CDM), we analytically calculate certain structure of the network and study the spread of epidemics on a growing network. Firstly, using CDM we calculate the degree distributions of three different growing models, which are the BA growing model, the preferential attachment accelerating growing model and the random attachment growing model. We obtain the evolution equation for the cumulative distribution function $F(k,t)$, and then obtain analytical results about $F(k,t)$ and the degree distribution $p(k,t)$. Secondly, we calculate the joint degree distribution $p(k_1, k_2, t)$ of the BA model by using the same method, thereby obtain the conditional degree distribution $p (k_1|k_2) $. We find that the BA model has no degree correlations. Finally, we consider the different states, susceptible and infected, according to the node health status. We establish the continuous degree SIS model on a static network and a growing network, respectively. We find that, in the case of growth, the new added health nodes can slightly reduce the ratio of infected nodes, but the final infected ratio will gradually tend to the final infected ratio of SIS model on static networks.

    Mathematics Subject Classification: Primary: 35R02; Secondary: 37N25.

    Citation:

    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  The degree distribution of the BA growing model. The marked points are the simulation results, and the corresponding solid lines are the analytical results. (a): $t=20000$ with $m_0=m=2, 4$ and 7. (b): $m_0=m=4$ with $t=200,2000,10000$ and 20000

    Figure 2.  The degree distribution of the preferential attachment accelerating growing model with $m$-varying. The marked points are the simulation results, and the corresponding solid lines are the analytical results. (a): $t=5000$, $\alpha=0.2$ with $m=2$, $4$ and $7$. (b): $t=5000$, $m=4$ with $\alpha=0.1$, $0.2$ and $0.3$. (c): $m=4$, $\alpha=0.2$ with $t=200,500,2000$ and 5000

    Figure 3.  The degree distribution of the random attachment growing model. The marked points are the simulation results, and the corresponding solid lines are the analytical results. (a): $t=20000$ with $m_0=m=2, 4$ and 7. (b): $m_0=m=4$ with $t=200,2000,10000$ and 20000

    Figure 4.  The schematic diagram of added directed edges $\Delta L_1$ and $\Delta L_2$

    Figure 5.  The ratio of infected nodes over time $t$ for the SIS model on static BA network with size $N=5000$. The marked lines are the average of 500 runs of stochastic simulations, and the corresponding solid lines are the results of numerical simulation. The initial ratio of infected nodes is set to 5%

    Figure 6.  The relative ratio of infected nodes with degree $k$ at time $t$ for the SIS model on static BA network. The time $t=1000$ and the initial ratio of infected nodes is 5%

    Figure 7.  The relative ratio of infected nodes with degree $k$ at time $t$ for the SIS model on BA growing network. The time $t=6000$, the initial time $t_0=5000$ and the initial ratio of infected nodes is 5%

    Figure 8.  The ratio of infected nodes over time $t$ for the SIS model on BA growing network with different epidemic occurrence time $t_0$. The time step $t$ is recorded from $t0$. The marked lines are the average of 200 runs of stochastic simulations, and the corresponding solid lines are the results of numerical simulation. (a): $t_0=150$; (b): $t_0=200$; (c): $t_0=1000$

    Table 1.  The definition of main notations

    NotationDefinition
    $m_0$The total number of nodes in the initial network.
    $l_0$The total number of edges in the initial network.
    $\Pi_i$The probability for the node $i$ connect to the new added node.
    $N(t)$The total number of nodes at time $t$.
    $\hat{N}(k, t)$The total number of nodes which degree not more than $k$ at time $t$ (note there has a hat $\text{ha}{{\text{t}}^{\text{ }\!\!\hat{\ }\!\!\text{ }}}$ on letter $N$).
    $F(k, t)$The cumulative distribution function of node degree, or the proportion of nodes which degree not more than $k$, at time $t$.
    $p(k, t)$The degree distribution, or the probability density of node which degree equal to $k$, at time $t$, $p(k, t)=\frac{\partial F(k, t)}{\partial k}$.
    $L(t)$The total number of directed edges at time $t$.
    $\hat{L}(k_1, k_2, t)$The total number of directed edges which degree sequentially not larger than $k_1$ and $k_2$ at time $t$ (also note the hat).
    $F(k_1, k_2, t)$The joint cumulative distribution function at time $t$.
    $p(k_1, k_2, t)$The joint degree distribution at time $t$, $p(k_1, k_2, t)=\frac{\partial^2 F(k_1, k_2, t)}{\partial k_1 \partial k_2}$.
    $p(k_2|k_1, t)$The conditional degree distribution at time $t$.
    $q(k, t)$The marginal distribution at time $t$.
    $F_S(k, t)$The cumulative distribution function of susceptible nodes at time $t$.
    $F_I(k, t)$The cumulative distribution function of infected nodes at time $t$.
    $p_S(k, t)$The probability density of susceptible nodes which degree equal to $k$ at time $t$.
    $p_I(k, t)$The probability density of infected nodes which degree equal to $k$ at time $t$.
    $\Theta_k$The probability that a edge emitted by degree $k$ node points to an infected node.
    $\Theta$The probability that a edge points to an infected node in degree unrelated network.
     | Show Table
    DownLoad: CSV
  • [1] R. Albert and A. L. Barabási, Statistical mechanics of complex networks, Reviews of Modern Physics, 74 (2002), 47-97.  doi: 10.1103/RevModPhys.74.47.
    [2] A. L. BarabásiR. Albert and H. Jeong, Mean-field theory for scale-free random networks, Physica A: Statistical Mechanics and its Applications, 272 (1999), 173-187. 
    [3] A. L. Barabási and R. Albert, Emergence of scaling in random networks, Science, 286 (1999), 509-512.  doi: 10.1126/science.286.5439.509.
    [4] S. N. Dorogovtsev, J. F. F. Mendes and A. N. Samukhin, Structure of growing networks with preferential linking, Physical Review Letters, 85 (2000), 4633. doi: 10.1103/PhysRevLett.85.4633.
    [5] S. N. Dorogovtsev and J. F. F. Mendes, Scaling properties of scale-free evolving networks: Continuous approach, Physical Review E, 63 (2001), 056125. doi: 10.1103/PhysRevE.63.056125.
    [6] S. N. Dorogovtsev and J. F. F. Mendes, Evolution of Networks: From Biological Nets to the Internet and WWW, Oxford University Press, New York, 2013.
    [7] P. Erdős and A. Rényi, On the strength of connectedness of a random graph, Acta Mathematica Hungarica, 12 (1961), 261-267.  doi: 10.1007/BF02066689.
    [8] M. FaloutsosP. Faloutsos and C. Faloutsos, On power-law relationships of the internet topology, ACM SIGCOMM Computer Communication Review, 29 (1999), 251-262.  doi: 10.1145/316188.316229.
    [9] M. J. Gagen and J. S. Mattick, Accelerating, hyperaccelerating, and decelerating networks, Physical Review E, 72 (2005), 016123. doi: 10.1103/PhysRevE.72.016123.
    [10] T. House and M. J. Keeling, Insights from unifying modern approximations to infections on networks, Journal of The Royal Society Interface, 8 (2011), 67-73.  doi: 10.1098/rsif.2010.0179.
    [11] M. J. Keeling, The effects of local spatial structure on epidemiological invasions, Proceedings of the Royal Society of London. Series B: Biological Sciences, 266 (1999), 859-867.  doi: 10.1098/rspb.1999.0716.
    [12] K. T. D. Ken and M. J. Keeling, Modeling dynamic and network heterogeneities in the spread of sexually transmitted diseases, Proceedings of the National Academy of Sciences, 99 (2002), 13330-13335. 
    [13] P. L. Krapivsky, S. Redner and F. Leyvraz, Connectivity of growing random networks, Physical Review Letters, 85 (2000), 4629.
    [14] P. L. Krapivsky and S. Redner, Organization of growing random networks, Physical Review E, 63 (2001), 066123. doi: 10.1103/PhysRevE.63.066123.
    [15] J. LindquistJ. MaP. van den Driessche and F. H. Willeboordse, Effective degree network disease models, Journal of Mathematical Biology, 62 (2011), 143-164.  doi: 10.1007/s00285-010-0331-2.
    [16] C. Liu, J. Xie, H. Chen, H. Zhang and M. Tang, Interplay between the local information based behavioral responses and the epidemic spreading in complex networks, Chaos: An Interdisciplinary Journal of Nonlinear Science, 25 (2015), 103111, 7 pp. doi: 10.1063/1.4931032.
    [17] S. Milgram, The small world problem, Psychology Today, 2 (1967), 60-67. 
    [18] J. C. MillerA. C. Slim and E. M. Volz, Edge-based compartmental modelling for infectious disease spread, Journal of the Royal Society Interface, 9 (2012), 890-906. 
    [19] J. C. Miller and I. Z. Kiss, Epidemic spread in networks: Existing methods and current challenges, Mathematical Modelling of Natural Phenomena, 9 (2014), 4-42.  doi: 10.1051/mmnp/20149202.
    [20] Y. MorenoR. Pastor-Satorras and A. Vespignani, Epidemic outbreaks in complex heterogeneous networks, The European Physical Journal B-Condensed Matter and Complex Systems, 26 (2002), 521-529.  doi: 10.1140/epjb/e20020122.
    [21] M. E. J. Newman, The structure and function of complex networks, SIAM Review, 45 (2003), 167-256.  doi: 10.1137/S003614450342480.
    [22] R. Pastor-Satorras and A. Vespignani, Epidemic spreading in scale-free networks, Physical Review Letters, 86 (2001), 3200. doi: 10.1103/PhysRevLett.86.3200.
    [23] D. Shi, Q. Chen and L. Liu, Markov chain-based numerical method for degree distributions of growing networks, Physical Review E, 71 (2005), 036140.
    [24] E. Volz, SIR dynamics in random networks with heterogeneous connectivity, Journal of Mathematical Biology, 56 (2008), 293-310.  doi: 10.1007/s00285-007-0116-4.
    [25] D. J. Watts and S. H. Strogatz, Collective dynamics of "small-world" networks, Nature, 393 (1998), 440-442. 
    [26] H. Zhang, J. Xie, M. Tang and Y. Lai, Suppression of epidemic spreading in complex networks by local information based behavioral responses, Chaos: An Interdisciplinary Journal of Nonlinear Science, 24 (2014), 043106, 7 pp. doi: 10.1063/1.4896333.
  • 加载中

Figures(8)

Tables(1)

SHARE

Article Metrics

HTML views(1702) PDF downloads(254) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return