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

A Fast Community Detection Algorithm based on Clustering Coefficient

Authors
Run Zheng
Corresponding Author
Run Zheng
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.100How to use a DOI?
Keywords
Community detection; Clustering coefficient.
Abstract
Community detecting has always been a hot topic in the complex network research area, the fast and accurate community detection can provide a good foundation for the research of complex network nature. With the number of network nodes increasing, the structure of network becomes complicated, the traditional community detection becomes more unpractical for they are all based on global information of network. We offered a local community detection algorithm which didn’t need to know the whole complex network information, it just beginning with a node as initial community and computing the intensity between the initial node and its adjacent nodes. And add its adjacent nodes to community gradually; eventually get this node community structure. Above all, this method can achieve global network community detection. We applied this method to American College football network and dolphin social network, and experiment results show accuracy and feasibility.
Open Access
This is an open access article distributed under the CC BY-NC license.

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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-19.2019.100How 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  - Run Zheng
PY  - 2019/04
DA  - 2019/04
TI  - A Fast Community Detection Algorithm based on Clustering Coefficient
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-19.2019.100
DO  - https://doi.org/10.2991/icmeit-19.2019.100
ID  - Zheng2019/04
ER  -