Parameter-Free Structural Diversity Search

Jinbin Huang; Xin HUANG; Yuanyuan Zhu; Jianliang XU

doi:10.1007/978-3-030-34223-4_43

Parameter-Free Structural Diversity Search

Jinbin Huang^*, Xin HUANG, Yuanyuan Zhu, Jianliang XU

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of which automatically models various kinds of social contexts without parameter t. Leveraging on and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings
Editors	Reynold Cheng, Nikos Mamoulis, Yizhou Sun, Xin Huang
Publisher	Springer
Pages	677-693
Number of pages	17
ISBN (Print)	9783030342227
DOIs	https://doi.org/10.1007/978-3-030-34223-4_43
Publication status	Published - 29 Oct 2019
Event	20th International Conference on Web Information Systems Engineering, WISE 2019 - , Hong Kong Duration: 19 Jan 2020 → 22 Jan 2020 https://link.springer.com/book/10.1007/978-3-030-34223-4 https://wise2019.comp.polyu.edu.hk/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11881 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Web Information Systems Engineering, WISE 2019
Country/Territory	Hong Kong
Period	19/01/20 → 22/01/20
Internet address	https://link.springer.com/book/10.1007/978-3-030-34223-4 https://wise2019.comp.polyu.edu.hk/

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-34223-4_43

Cite this

Huang, J., HUANG, X., Zhu, Y., & XU, J. (2019). Parameter-Free Structural Diversity Search. In R. Cheng, N. Mamoulis, Y. Sun, & X. Huang (Eds.), Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings (pp. 677-693). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11881 LNCS). Springer. https://doi.org/10.1007/978-3-030-34223-4_43

Huang, Jinbin ; HUANG, Xin ; Zhu, Yuanyuan et al. / Parameter-Free Structural Diversity Search. Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings. editor / Reynold Cheng ; Nikos Mamoulis ; Yizhou Sun ; Xin Huang. Springer, 2019. pp. 677-693 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Parameter-Free Structural Diversity Search",

abstract = "The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of which automatically models various kinds of social contexts without parameter t. Leveraging on and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.",

author = "Jinbin Huang and Xin HUANG and Yuanyuan Zhu and Jianliang XU",

note = "Funding Information: This work is supported by the NSFC Nos. 61702435, 61972291, RGC Nos. 12200917, 12200817, CRF C6030-18GF, and the National Science Foundation of Hubei Province No. 2018CFB519.; 20th International Conference on Web Information Systems Engineering, WISE 2019 ; Conference date: 19-01-2020 Through 22-01-2020",

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month = oct,

day = "29",

doi = "10.1007/978-3-030-34223-4_43",

language = "English",

isbn = "9783030342227",

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

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pages = "677--693",

editor = "Reynold Cheng and Nikos Mamoulis and Yizhou Sun and Xin Huang",

booktitle = "Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings",

url = "https://link.springer.com/book/10.1007/978-3-030-34223-4, https://wise2019.comp.polyu.edu.hk/",

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Huang, J, HUANG, X, Zhu, Y & XU, J 2019, Parameter-Free Structural Diversity Search. in R Cheng, N Mamoulis, Y Sun & X Huang (eds), Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11881 LNCS, Springer, pp. 677-693, 20th International Conference on Web Information Systems Engineering, WISE 2019, Hong Kong, 19/01/20. https://doi.org/10.1007/978-3-030-34223-4_43

Parameter-Free Structural Diversity Search. / Huang, Jinbin; HUANG, Xin; Zhu, Yuanyuan et al.

Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings. ed. / Reynold Cheng; Nikos Mamoulis; Yizhou Sun; Xin Huang. Springer, 2019. p. 677-693 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11881 LNCS).

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Parameter-Free Structural Diversity Search

AU - Huang, Jinbin

AU - HUANG, Xin

AU - Zhu, Yuanyuan

AU - XU, Jianliang

N1 - Funding Information: This work is supported by the NSFC Nos. 61702435, 61972291, RGC Nos. 12200917, 12200817, CRF C6030-18GF, and the National Science Foundation of Hubei Province No. 2018CFB519.

PY - 2019/10/29

Y1 - 2019/10/29

N2 - The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of which automatically models various kinds of social contexts without parameter t. Leveraging on and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.

AB - The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of which automatically models various kinds of social contexts without parameter t. Leveraging on and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.

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DO - 10.1007/978-3-030-34223-4_43

M3 - Conference contribution

AN - SCOPUS:85077007022

SN - 9783030342227

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

SP - 677

EP - 693

BT - Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings

A2 - Cheng, Reynold

A2 - Mamoulis, Nikos

A2 - Sun, Yizhou

A2 - Huang, Xin

PB - Springer

T2 - 20th International Conference on Web Information Systems Engineering, WISE 2019

Y2 - 19 January 2020 through 22 January 2020

ER -

Huang J, HUANG X, Zhu Y, XU J. Parameter-Free Structural Diversity Search. In Cheng R, Mamoulis N, Sun Y, Huang X, editors, Web Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings. Springer. 2019. p. 677-693. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-34223-4_43

Parameter-Free Structural Diversity Search

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