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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2017-06-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-017-03613-z |
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doaj-5ecf5f1bb27e4bd7888f5b36e7065a072020-12-08T03:15:19ZengNature Publishing GroupScientific Reports2045-23222017-06-017111210.1038/s41598-017-03613-zA Unified Framework for Complex Networks with Degree Trichotomy Based on Markov ChainsDavid Shui Wing Hui0Yi-Chao Chen1Gong Zhang2Weijie Wu3Guanrong Chen4John C. S. Lui5Yingtao Li6Huawei Technologies Co. Ltd.Huawei Technologies Co. Ltd.Huawei Technologies Co. Ltd.Huawei Technologies Co. Ltd.City University of Hong KongThe Chinese University of Hong KongHuawei Technologies Co. Ltd.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.https://doi.org/10.1038/s41598-017-03613-z |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Shui Wing Hui Yi-Chao Chen Gong Zhang Weijie Wu Guanrong Chen John C. S. Lui Yingtao Li |
spellingShingle |
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 Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2017-06-01 |
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. |
url |
https://doi.org/10.1038/s41598-017-03613-z |
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