Dynamic Community Detection Algorithm Based On Hidden Markov Model

DOI

10.2991/isaeece-16.2016.55How to use a DOI?

Keywords

Dynamic Social Network; Hidden Markov Model; Community Structure

Abstract

The HMM_DC algorithm is proposed based on the Hidden Markov Model to detect the community in dynamic social network. The algorithm transforms the community detection problem to get the optimal status chain in Hidden Markov Model with considering the history information and characteristics in dynamic social network. The algorithm uses the observed chain and status chain to represent the community structure and node information and can identify the community structure without extra information. The experiment results show that HMM_DC algorithm is available and performs effectively and accurately in identifying the community structure in the dynamic social network and the value of Q and NMI can raise 28% and 20% at least.

Copyright

© 2016, 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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TY  - CONF
AU  - Zhe Dong
PY  - 2016/04
DA  - 2016/04
TI  - Dynamic Community Detection Algorithm Based On Hidden Markov Model
BT  - Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 288
EP  - 294
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-16.2016.55
DO  - 10.2991/isaeece-16.2016.55
ID  - Dong2016/04
ER  -