Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

The Relations Between Classic and Geometric Probability and Scale-Free Feature In Social Networks

Authors
Fei MA, Jing SU, Bing YAO
Corresponding Author
Fei MA
Available Online December 2016.
DOI
10.2991/cnct-16.2017.23How to use a DOI?
Keywords
Probability, Scale-free, Power-law, Models, Ddifferential equation.
Abstract

Probability and statistics have been proved to be strong and useful tools for recovering the topological properties in complex systems and networks all over the world. We recall the process of studying complex systems in the past decades and propose general evolution equation about the dynamical function of network. Relations between classic and geometric probability and scale-free feature have been discussed, and we build up a class new network models in which the classic and geometric probability are coexisting.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
10.2991/cnct-16.2017.23
ISSN
2352-538X
DOI
10.2991/cnct-16.2017.23How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fei MA
AU  - Jing SU
AU  - Bing YAO
PY  - 2016/12
DA  - 2016/12
TI  - The Relations Between Classic and Geometric Probability and Scale-Free Feature In Social Networks
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 171
EP  - 178
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.23
DO  - 10.2991/cnct-16.2017.23
ID  - MA2016/12
ER  -