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

David Shui Wing Hui; Yi-Chao Chen; Gong Zhang; Weijie Wu; Guanrong Chen; John C. S. Lui; Yingtao Li

doi:10.1038/s41598-017-03613-z

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

David Shui Wing Hui, Yi-Chao Chen, Gong Zhang, Weijie Wu^*, Guanrong Chen, John C. S. Lui, Yingtao Li

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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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.

Original language	English
Article number	3723
Journal	Scientific Reports
Volume	7
Issue number	1
Online published	16 Jun 2017
DOIs	https://doi.org/10.1038/s41598-017-03613-z
Publication status	Published - 2017

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Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

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title = "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 variousapplications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.",

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AU - Chen, Guanrong

AU - Lui, John C. S.

AU - Li, Yingtao

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N2 - 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 variousapplications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.

AB - 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 variousapplications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.

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

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