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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, 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.

SUBMITTER: Hui DSW 

PROVIDER: S-EPMC5473852 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.

Hui David Shui Wing DSW   Chen Yi-Chao YC   Zhang Gong G   Wu Weijie W   Chen Guanrong G   Lui John C S JCS   Li Yingtao Y  

Scientific reports 20170616 1


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  ...[more]

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