Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties
Networks are prevalent in real life, and the study of network evolution models is very important for understanding the nature and laws of real networks. The distribution of the initial degree of nodes in existing classical models is constant or uniform. The model we proposed shows binomial distribut...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2023/5533260 |
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| _version_ | 1849470951921549312 |
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| author | Chuankui Yan |
| author_facet | Chuankui Yan |
| author_sort | Chuankui Yan |
| collection | DOAJ |
| description | Networks are prevalent in real life, and the study of network evolution models is very important for understanding the nature and laws of real networks. The distribution of the initial degree of nodes in existing classical models is constant or uniform. The model we proposed shows binomial distribution, and it is consistent with real network data. The theoretical analysis shows that the proposed model is scale-free at different probability values and its clustering coefficients are adjustable, and the Barabasi-Albert model is a special case of p=0 in our model. In addition, the analytical results of the clustering coefficients can be estimated using mean-field theory. The mean clustering coefficients calculated from the simulated data and the analytical results tend to be stable. The model also exhibits small-world characteristics and has good reproducibility for short distances of real networks. Our model combines three network characteristics, scale-free, high clustering coefficients, and small-world characteristics, which is a significant improvement over traditional models with only a single or two characteristics. The theoretical analysis procedure can be used as a theoretical reference for various network models to study the estimation of clustering coefficients. The existence of stable equilibrium points of the model explains the controversy of whether scale-free is universal or not, and this explanation provides a new way of thinking to understand the problem. |
| format | Article |
| id | doaj-art-da6538e46d604e85a4eeddb49d343d94 |
| institution | Kabale University |
| issn | 1687-0042 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-da6538e46d604e85a4eeddb49d343d942025-08-20T03:24:59ZengWileyJournal of Applied Mathematics1687-00422023-01-01202310.1155/2023/5533260Network Model with Scale-Free, High Clustering Coefficients, and Small-World PropertiesChuankui Yan0College of Mathematics and PhysicsNetworks are prevalent in real life, and the study of network evolution models is very important for understanding the nature and laws of real networks. The distribution of the initial degree of nodes in existing classical models is constant or uniform. The model we proposed shows binomial distribution, and it is consistent with real network data. The theoretical analysis shows that the proposed model is scale-free at different probability values and its clustering coefficients are adjustable, and the Barabasi-Albert model is a special case of p=0 in our model. In addition, the analytical results of the clustering coefficients can be estimated using mean-field theory. The mean clustering coefficients calculated from the simulated data and the analytical results tend to be stable. The model also exhibits small-world characteristics and has good reproducibility for short distances of real networks. Our model combines three network characteristics, scale-free, high clustering coefficients, and small-world characteristics, which is a significant improvement over traditional models with only a single or two characteristics. The theoretical analysis procedure can be used as a theoretical reference for various network models to study the estimation of clustering coefficients. The existence of stable equilibrium points of the model explains the controversy of whether scale-free is universal or not, and this explanation provides a new way of thinking to understand the problem.http://dx.doi.org/10.1155/2023/5533260 |
| spellingShingle | Chuankui Yan Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties Journal of Applied Mathematics |
| title | Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties |
| title_full | Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties |
| title_fullStr | Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties |
| title_full_unstemmed | Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties |
| title_short | Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties |
| title_sort | network model with scale free high clustering coefficients and small world properties |
| url | http://dx.doi.org/10.1155/2023/5533260 |
| work_keys_str_mv | AT chuankuiyan networkmodelwithscalefreehighclusteringcoefficientsandsmallworldproperties |