Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks

Jianan Zhao; Ding Xiao; Linmei Hu; Chuan Shi

doi:10.1007/978-3-030-26072-9_7

Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks

Jianan Zhao, Ding Xiao, Linmei Hu, Chuan Shi^*

^*Corresponding author for this work

Beijing University of Posts and Telecommunications

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Heterogeneous Information Network (HIN) has been widely adopted in various tasks due to its excellence in modeling complex network data. To handle the additional attributes of nodes in HIN, the Attributed Heterogeneous Information Network (AHIN) was brought forward. Recently, clustering on HIN becomes a hot topic, since it is useful in many applications. Although existing semi-supervised clustering methods in HIN have achieved performance improvements to some extent, these models seldom consider the correlations among attributes which typically exist in real applications. To tackle this issue, we propose a novel model SCAN for semi-supervised clustering in AHIN. Our model captures the coupling relations between mixed types of node attributes and therefore obtains better attribute similarity. Moreover, we propose a flexible constraint method to leverage supervised information and network information for flexible adaption of different datasets and clustering objectives. Extensive experiments have shown that our model outperforms state-of-the-art algorithms.

Original language	English
Title of host publication	Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings
Editors	Jie Shao, Man Lung Yiu, Masashi Toyoda, Dongxiang Zhang, Wei Wang, Bin Cui
Publisher	Springer Verlag
Pages	95-109
Number of pages	15
ISBN (Print)	9783030260712
DOIs	https://doi.org/10.1007/978-3-030-26072-9_7
Publication status	Published - 2019
Externally published	Yes
Event	3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 - Chengdu, China Duration: 1 Aug 2019 → 3 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11641 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019
Country/Territory	China
City	Chengdu
Period	1/08/19 → 3/08/19

Keywords

Attributed Heterogeneous Information Network
Coupled Attributes
Semi-supervised clustering

Access to Document

10.1007/978-3-030-26072-9_7

Cite this

Zhao, J., Xiao, D., Hu, L., & Shi, C. (2019). Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks. In J. Shao, M. L. Yiu, M. Toyoda, D. Zhang, W. Wang, & B. Cui (Eds.), Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings (pp. 95-109). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11641 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-26072-9_7

Zhao, Jianan ; Xiao, Ding ; Hu, Linmei et al. / Coupled semi-supervised clustering : Exploring attribute correlations in heterogeneous information networks. Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings. editor / Jie Shao ; Man Lung Yiu ; Masashi Toyoda ; Dongxiang Zhang ; Wei Wang ; Bin Cui. Springer Verlag, 2019. pp. 95-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{532df99c43004ef0b3ee39176789f01b,

title = "Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks",

abstract = "Heterogeneous Information Network (HIN) has been widely adopted in various tasks due to its excellence in modeling complex network data. To handle the additional attributes of nodes in HIN, the Attributed Heterogeneous Information Network (AHIN) was brought forward. Recently, clustering on HIN becomes a hot topic, since it is useful in many applications. Although existing semi-supervised clustering methods in HIN have achieved performance improvements to some extent, these models seldom consider the correlations among attributes which typically exist in real applications. To tackle this issue, we propose a novel model SCAN for semi-supervised clustering in AHIN. Our model captures the coupling relations between mixed types of node attributes and therefore obtains better attribute similarity. Moreover, we propose a flexible constraint method to leverage supervised information and network information for flexible adaption of different datasets and clustering objectives. Extensive experiments have shown that our model outperforms state-of-the-art algorithms.",

keywords = "Attributed Heterogeneous Information Network, Coupled Attributes, Semi-supervised clustering",

author = "Jianan Zhao and Ding Xiao and Linmei Hu and Chuan Shi",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 ; Conference date: 01-08-2019 Through 03-08-2019",

year = "2019",

doi = "10.1007/978-3-030-26072-9_7",

language = "English",

isbn = "9783030260712",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "95--109",

editor = "Jie Shao and Yiu, {Man Lung} and Masashi Toyoda and Dongxiang Zhang and Wei Wang and Bin Cui",

booktitle = "Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings",

address = "Germany",

}

Zhao, J, Xiao, D, Hu, L & Shi, C 2019, Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks. in J Shao, ML Yiu, M Toyoda, D Zhang, W Wang & B Cui (eds), Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11641 LNCS, Springer Verlag, pp. 95-109, 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019, Chengdu, China, 1/08/19. https://doi.org/10.1007/978-3-030-26072-9_7

Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks. / Zhao, Jianan; Xiao, Ding; Hu, Linmei et al.
Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings. ed. / Jie Shao; Man Lung Yiu; Masashi Toyoda; Dongxiang Zhang; Wei Wang; Bin Cui. Springer Verlag, 2019. p. 95-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11641 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Coupled semi-supervised clustering

T2 - 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019

AU - Zhao, Jianan

AU - Xiao, Ding

AU - Hu, Linmei

AU - Shi, Chuan

PY - 2019

Y1 - 2019

N2 - Heterogeneous Information Network (HIN) has been widely adopted in various tasks due to its excellence in modeling complex network data. To handle the additional attributes of nodes in HIN, the Attributed Heterogeneous Information Network (AHIN) was brought forward. Recently, clustering on HIN becomes a hot topic, since it is useful in many applications. Although existing semi-supervised clustering methods in HIN have achieved performance improvements to some extent, these models seldom consider the correlations among attributes which typically exist in real applications. To tackle this issue, we propose a novel model SCAN for semi-supervised clustering in AHIN. Our model captures the coupling relations between mixed types of node attributes and therefore obtains better attribute similarity. Moreover, we propose a flexible constraint method to leverage supervised information and network information for flexible adaption of different datasets and clustering objectives. Extensive experiments have shown that our model outperforms state-of-the-art algorithms.

AB - Heterogeneous Information Network (HIN) has been widely adopted in various tasks due to its excellence in modeling complex network data. To handle the additional attributes of nodes in HIN, the Attributed Heterogeneous Information Network (AHIN) was brought forward. Recently, clustering on HIN becomes a hot topic, since it is useful in many applications. Although existing semi-supervised clustering methods in HIN have achieved performance improvements to some extent, these models seldom consider the correlations among attributes which typically exist in real applications. To tackle this issue, we propose a novel model SCAN for semi-supervised clustering in AHIN. Our model captures the coupling relations between mixed types of node attributes and therefore obtains better attribute similarity. Moreover, we propose a flexible constraint method to leverage supervised information and network information for flexible adaption of different datasets and clustering objectives. Extensive experiments have shown that our model outperforms state-of-the-art algorithms.

KW - Attributed Heterogeneous Information Network

KW - Coupled Attributes

KW - Semi-supervised clustering

UR - http://www.scopus.com/inward/record.url?scp=85069998960&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-26072-9_7

DO - 10.1007/978-3-030-26072-9_7

M3 - Conference contribution

AN - SCOPUS:85069998960

SN - 9783030260712

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 95

EP - 109

BT - Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings

A2 - Shao, Jie

A2 - Yiu, Man Lung

A2 - Toyoda, Masashi

A2 - Zhang, Dongxiang

A2 - Wang, Wei

A2 - Cui, Bin

PB - Springer Verlag

Y2 - 1 August 2019 through 3 August 2019

ER -

Zhao J, Xiao D, Hu L, Shi C. Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks. In Shao J, Yiu ML, Toyoda M, Zhang D, Wang W, Cui B, editors, Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings. Springer Verlag. 2019. p. 95-109. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-26072-9_7

Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks

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Keywords

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