Proceedings of the The 1st International Workshop on Cloud Computing and Information Security

A method based on compressive sensing to detect community structure using deep belief network

Authors
Zhang Liangliang, Wu Haijia, Feng Jing, Zhang Xiongwei
Corresponding Author
Zhang Liangliang
Available Online November 2013.
DOI
10.2991/ccis-13.2013.3How to use a DOI?
Keywords
compressive sensing; community structure; social network; deep belief network(DBN)
Abstract

A deep learning scheme based on compressive sensing to detect community structure of large-scale social network is presented. Our contributions in this work are as follows: First, we reduced the high-dimensional feature of social media data via compressive sensing by using random measurement matrix; Second, deep belief network is employed to learn unsupervised from the low-dimensional samples; Finally the model is fine-tuned by supervised learning from a small scale sample sets with class labels. The effectiveness of the proposed scheme is confirmed by the experiment results.

Copyright
© 2013, 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 The 1st International Workshop on Cloud Computing and Information Security
Series
Advances in Intelligent Systems Research
Publication Date
November 2013
ISBN
10.2991/ccis-13.2013.3
ISSN
1951-6851
DOI
10.2991/ccis-13.2013.3How to use a DOI?
Copyright
© 2013, 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  - Zhang Liangliang
AU  - Wu Haijia
AU  - Feng Jing
AU  - Zhang Xiongwei
PY  - 2013/11
DA  - 2013/11
TI  - A method based on compressive sensing to detect community structure using deep belief network
BT  - Proceedings of the The 1st International Workshop on Cloud Computing and Information Security
PB  - Atlantis Press
SP  - 10
EP  - 13
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccis-13.2013.3
DO  - 10.2991/ccis-13.2013.3
ID  - Liangliang2013/11
ER  -