IEICE TRANSACTIONS on Information
Community Discovery on Multi-View Social Networks via Joint Regularized Nonnegative Matrix Triple Factorization
Summary :
In multi-view social networks field, a flexible Nonnegative Matrix Factorization (NMF) based framework is proposed which integrates multi-view relation data and feature data for community discovery. Benefit with a relaxed pairwise regularization and a novel orthogonal regularization, it outperforms the-state-of-art algorithms on five real-world datasets in terms of accuracy and NMI.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.6 pp.1262-1270
- Publication Date
- 2017/06/01
- Publicized
- 2017/03/21
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7004
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Liangliang ZHANG
PLA University of Science and Technology
Longqi YANG
PLA University of Science and Technology
Yong GONG
PLA University of Science and Technology
Zhisong PAN
PLA University of Science and Technology
Yanyan ZHANG
PLA University of Science and Technology
Guyu HU
PLA University of Science and Technology
Keyword
Latest Issue
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Liangliang ZHANG, Longqi YANG, Yong GONG, Zhisong PAN, Yanyan ZHANG, Guyu HU, "Community Discovery on Multi-View Social Networks via Joint Regularized Nonnegative Matrix Triple Factorization" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 6, pp. 1262-1270, June 2017, doi: 10.1587/transinf.2017EDP7004.
Abstract: In multi-view social networks field, a flexible Nonnegative Matrix Factorization (NMF) based framework is proposed which integrates multi-view relation data and feature data for community discovery. Benefit with a relaxed pairwise regularization and a novel orthogonal regularization, it outperforms the-state-of-art algorithms on five real-world datasets in terms of accuracy and NMI.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7004/_p
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@ARTICLE{e100-d_6_1262,
author={Liangliang ZHANG, Longqi YANG, Yong GONG, Zhisong PAN, Yanyan ZHANG, Guyu HU, },
journal={IEICE TRANSACTIONS on Information},
title={Community Discovery on Multi-View Social Networks via Joint Regularized Nonnegative Matrix Triple Factorization},
year={2017},
volume={E100-D},
number={6},
pages={1262-1270},
abstract={In multi-view social networks field, a flexible Nonnegative Matrix Factorization (NMF) based framework is proposed which integrates multi-view relation data and feature data for community discovery. Benefit with a relaxed pairwise regularization and a novel orthogonal regularization, it outperforms the-state-of-art algorithms on five real-world datasets in terms of accuracy and NMI.},
keywords={},
doi={10.1587/transinf.2017EDP7004},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Community Discovery on Multi-View Social Networks via Joint Regularized Nonnegative Matrix Triple Factorization
T2 - IEICE TRANSACTIONS on Information
SP - 1262
EP - 1270
AU - Liangliang ZHANG
AU - Longqi YANG
AU - Yong GONG
AU - Zhisong PAN
AU - Yanyan ZHANG
AU - Guyu HU
PY - 2017
DO - 10.1587/transinf.2017EDP7004
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2017
AB - In multi-view social networks field, a flexible Nonnegative Matrix Factorization (NMF) based framework is proposed which integrates multi-view relation data and feature data for community discovery. Benefit with a relaxed pairwise regularization and a novel orthogonal regularization, it outperforms the-state-of-art algorithms on five real-world datasets in terms of accuracy and NMI.
ER -
English
