Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Coreness Tunable Network Model

Authors
Yifan Wang
Corresponding Author
Yifan Wang
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.31How to use a DOI?
Keywords
Coreness Tunable Network Model; Shell Distribution; SIR Model.
Abstract
Kitsak et al argued that nodes dwell in diverse network shells by a k-core decomposition process show more reliable identification for nodal importance which had attracted more and more attentions in different domains. But one seldom focuses on the distribution of node numbers (DNN) in different shells of a network, experiment results show regular characteristics. While, the existing theoretical network models, such as BA scale-free model, WS Small-world model, ER random network model et al cannot reproduce the features. To fill this gap, a group of coreness tunable network (CTN) models are proposed, in which the coreness of each node is totally controllable. The CTN has a similar network performance compared to real-world by counting basic static geometric features and spreading performance under SIR model. Our CTN models are providing a theoretical framework to deepen humans’ understanding of the coreness structure and function of complex networks.
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Proceedings
3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.31How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yifan Wang
PY  - 2019/04
DA  - 2019/04
TI  - Coreness Tunable Network Model
BT  - 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.31
DO  - https://doi.org/10.2991/icmeit-19.2019.31
ID  - Wang2019/04
ER  -