Structural calculations and propagation modeling of growing networks based on continuous degree
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, u...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2017-09-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2017062 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590057428484096 |
|---|---|
| author | Junbo Jia Zhen Jin Lili Chang Xinchu Fu |
| author_facet | Junbo Jia Zhen Jin Lili Chang Xinchu Fu |
| author_sort | Junbo Jia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-756bbe3620074ee3bc8693add87e06fe |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2017-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-756bbe3620074ee3bc8693add87e06fe2025-01-24T02:40:31ZengAIMS PressMathematical Biosciences and Engineering1551-00182017-09-01145&61215123210.3934/mbe.2017062Structural calculations and propagation modeling of growing networks based on continuous degreeJunbo Jia0Zhen Jin1Lili Chang2Xinchu Fu3. Department of Mathematics, Shanghai University, Shanghai 200444, China. Department of Mathematics, Shanghai University, Shanghai 200444, China. Department of Mathematics, Shanghai University, Shanghai 200444, China. Department of Mathematics, Shanghai University, Shanghai 200444, ChinaWhen 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.https://www.aimspress.com/article/doi/10.3934/mbe.2017062epidemic modelspropagation modelingcomplex networksgrowing networkspartial differential equation |
| spellingShingle | Junbo Jia Zhen Jin Lili Chang Xinchu Fu Structural calculations and propagation modeling of growing networks based on continuous degree Mathematical Biosciences and Engineering epidemic models propagation modeling complex networks growing networks partial differential equation |
| title | Structural calculations and propagation modeling of growing networks based on continuous degree |
| title_full | Structural calculations and propagation modeling of growing networks based on continuous degree |
| title_fullStr | Structural calculations and propagation modeling of growing networks based on continuous degree |
| title_full_unstemmed | Structural calculations and propagation modeling of growing networks based on continuous degree |
| title_short | Structural calculations and propagation modeling of growing networks based on continuous degree |
| title_sort | structural calculations and propagation modeling of growing networks based on continuous degree |
| topic | epidemic models propagation modeling complex networks growing networks partial differential equation |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2017062 |
| work_keys_str_mv | AT junbojia structuralcalculationsandpropagationmodelingofgrowingnetworksbasedoncontinuousdegree AT zhenjin structuralcalculationsandpropagationmodelingofgrowingnetworksbasedoncontinuousdegree AT lilichang structuralcalculationsandpropagationmodelingofgrowingnetworksbasedoncontinuousdegree AT xinchufu structuralcalculationsandpropagationmodelingofgrowingnetworksbasedoncontinuousdegree |