IEICE TRANSACTIONS on Fundamentals
Detecting Semantic Communities in Social Networks
Summary :
Community detection is an important task in the social network analysis field. Many detection methods have been developed; however, they provide little semantic interpretation for the discovered communities. We develop a framework based on joint matrix factorization to integrate network topology and node content information, such that the communities and their semantic labels are derived simultaneously. Moreover, to improve the detection accuracy, we attempt to make the community relationships derived from two types of information consistent. Experimental results on real-world networks show the superior performance of the proposed method and demonstrate its ability to semantically annotate communities.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.11 pp.2507-2512
- Publication Date
- 2017/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.2507
- Type of Manuscript
- LETTER
- Category
- Graphs and Networks
Authors
Zhen LI
PLA University of Science & Technology
Zhisong PAN
PLA University of Science & Technology
Guyu HU
PLA University of Science & Technology
Guopeng LI
Xi'an Communications Institute
Xingyu ZHOU
PLA University of Science & Technology
Keyword
Latest Issue
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Zhen LI, Zhisong PAN, Guyu HU, Guopeng LI, Xingyu ZHOU, "Detecting Semantic Communities in Social Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 11, pp. 2507-2512, November 2017, doi: 10.1587/transfun.E100.A.2507.
Abstract: Community detection is an important task in the social network analysis field. Many detection methods have been developed; however, they provide little semantic interpretation for the discovered communities. We develop a framework based on joint matrix factorization to integrate network topology and node content information, such that the communities and their semantic labels are derived simultaneously. Moreover, to improve the detection accuracy, we attempt to make the community relationships derived from two types of information consistent. Experimental results on real-world networks show the superior performance of the proposed method and demonstrate its ability to semantically annotate communities.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.2507/_p
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@ARTICLE{e100-a_11_2507,
author={Zhen LI, Zhisong PAN, Guyu HU, Guopeng LI, Xingyu ZHOU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Detecting Semantic Communities in Social Networks},
year={2017},
volume={E100-A},
number={11},
pages={2507-2512},
abstract={Community detection is an important task in the social network analysis field. Many detection methods have been developed; however, they provide little semantic interpretation for the discovered communities. We develop a framework based on joint matrix factorization to integrate network topology and node content information, such that the communities and their semantic labels are derived simultaneously. Moreover, to improve the detection accuracy, we attempt to make the community relationships derived from two types of information consistent. Experimental results on real-world networks show the superior performance of the proposed method and demonstrate its ability to semantically annotate communities.},
keywords={},
doi={10.1587/transfun.E100.A.2507},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Detecting Semantic Communities in Social Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2507
EP - 2512
AU - Zhen LI
AU - Zhisong PAN
AU - Guyu HU
AU - Guopeng LI
AU - Xingyu ZHOU
PY - 2017
DO - 10.1587/transfun.E100.A.2507
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2017
AB - Community detection is an important task in the social network analysis field. Many detection methods have been developed; however, they provide little semantic interpretation for the discovered communities. We develop a framework based on joint matrix factorization to integrate network topology and node content information, such that the communities and their semantic labels are derived simultaneously. Moreover, to improve the detection accuracy, we attempt to make the community relationships derived from two types of information consistent. Experimental results on real-world networks show the superior performance of the proposed method and demonstrate its ability to semantically annotate communities.
ER -
English
