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

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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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  -