A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains
Abstract This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the “trichotomy” observed in degree distributions,...
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Nature Portfolio
2017
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oai:doaj.org-article:5ecf5f1bb27e4bd7888f5b36e7065a072021-12-02T15:05:52ZA Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains10.1038/s41598-017-03613-z2045-2322https://doaj.org/article/5ecf5f1bb27e4bd7888f5b36e7065a072017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03613-zhttps://doaj.org/toc/2045-2322Abstract This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the “trichotomy” observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical models. Implications of the model to various applications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.David Shui Wing HuiYi-Chao ChenGong ZhangWeijie WuGuanrong ChenJohn C. S. LuiYingtao LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017) |
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DOAJ |
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DOAJ |
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EN |
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Medicine R Science Q |
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Medicine R Science Q David Shui Wing Hui Yi-Chao Chen Gong Zhang Weijie Wu Guanrong Chen John C. S. Lui Yingtao Li A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains |
| description |
Abstract This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the “trichotomy” observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical models. Implications of the model to various applications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper. |
| format |
article |
| author |
David Shui Wing Hui Yi-Chao Chen Gong Zhang Weijie Wu Guanrong Chen John C. S. Lui Yingtao Li |
| author_facet |
David Shui Wing Hui Yi-Chao Chen Gong Zhang Weijie Wu Guanrong Chen John C. S. Lui Yingtao Li |
| author_sort |
David Shui Wing Hui |
| title |
A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains |
| title_short |
A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains |
| title_full |
A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains |
| title_fullStr |
A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains |
| title_full_unstemmed |
A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains |
| title_sort |
unified framework for complex networks with degree trichotomy based on markov chains |
| publisher |
Nature Portfolio |
| publishDate |
2017 |
| url |
https://doaj.org/article/5ecf5f1bb27e4bd7888f5b36e7065a07 |
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