Uncovering attribute-driven active intimate communities

Md Musfique Anwar; Chengfei Liu; Jianxin Li

doi:10.1007/978-3-319-92013-9_9

Uncovering attribute-driven active intimate communities

Md Musfique Anwar^*, Chengfei Liu, Jianxin Li

^*Corresponding author for this work

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

9 Scopus citations

Abstract

Most existing studies in community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. However, the bulk of literature ignores the interaction strength among the users in the retrieved communities. As a result, many members of the detected communities do not interact frequently to each other. This inactivity will create problem for online advertisers as they require the community to be highly interactive to efficiently diffuse marketing information. In this paper, we study the problem of detecting attribute-driven active intimate community, that is, for a given input query consisting a set of attributes, we want to find densely-connected communities in which community members actively participate as well as have strong interaction (intimacy) with respect to the given query attributes. We design a novel attribute relevance intimacy score function for the detected communities and establish its desirable properties. To this end, we use an indexed based solution to efficiently discover active intimate communities. Extensive experiments on real data sets show the effectiveness and performance of our proposed method.

Original language	English
Title of host publication	Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings
Editors	Junhu Wang, Gao Cong, Jinjun Chen, Jianzhong Qi
Publisher	Springer Verlag
Pages	109-122
Number of pages	14
ISBN (Print)	9783319920122
DOIs	https://doi.org/10.1007/978-3-319-92013-9_9
State	Published - 2018
Externally published	Yes
Event	29th Australasian Database Conference, ADC 2018 - Gold Coast, Australia Duration: 24 May 2018 → 27 May 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10837 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	29th Australasian Database Conference, ADC 2018
Country/Territory	Australia
City	Gold Coast
Period	24/05/18 → 27/05/18

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

Keywords

Information diffusion
Intimacy score
Intimate community

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-92013-9_9

Cite this

Anwar, M. M., Liu, C., & Li, J. (2018). Uncovering attribute-driven active intimate communities. In J. Wang, G. Cong, J. Chen, & J. Qi (Eds.), Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings (pp. 109-122). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10837 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-92013-9_9

Anwar, Md Musfique ; Liu, Chengfei ; Li, Jianxin. / Uncovering attribute-driven active intimate communities. Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings. editor / Junhu Wang ; Gao Cong ; Jinjun Chen ; Jianzhong Qi. Springer Verlag, 2018. pp. 109-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Uncovering attribute-driven active intimate communities",

abstract = "Most existing studies in community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. However, the bulk of literature ignores the interaction strength among the users in the retrieved communities. As a result, many members of the detected communities do not interact frequently to each other. This inactivity will create problem for online advertisers as they require the community to be highly interactive to efficiently diffuse marketing information. In this paper, we study the problem of detecting attribute-driven active intimate community, that is, for a given input query consisting a set of attributes, we want to find densely-connected communities in which community members actively participate as well as have strong interaction (intimacy) with respect to the given query attributes. We design a novel attribute relevance intimacy score function for the detected communities and establish its desirable properties. To this end, we use an indexed based solution to efficiently discover active intimate communities. Extensive experiments on real data sets show the effectiveness and performance of our proposed method.",

keywords = "Information diffusion, Intimacy score, Intimate community",

author = "Anwar, \{Md Musfique\} and Chengfei Liu and Jianxin Li",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 29th Australasian Database Conference, ADC 2018 ; Conference date: 24-05-2018 Through 27-05-2018",

year = "2018",

doi = "10.1007/978-3-319-92013-9\_9",

language = "English",

isbn = "9783319920122",

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

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Anwar, MM, Liu, C & Li, J 2018, Uncovering attribute-driven active intimate communities. in J Wang, G Cong, J Chen & J Qi (eds), Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10837 LNCS, Springer Verlag, pp. 109-122, 29th Australasian Database Conference, ADC 2018, Gold Coast, Australia, 24/05/18. https://doi.org/10.1007/978-3-319-92013-9_9

Uncovering attribute-driven active intimate communities. / Anwar, Md Musfique; Liu, Chengfei; Li, Jianxin.
Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings. ed. / Junhu Wang; Gao Cong; Jinjun Chen; Jianzhong Qi. Springer Verlag, 2018. p. 109-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10837 LNCS).

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

TY - GEN

T1 - Uncovering attribute-driven active intimate communities

AU - Anwar, Md Musfique

AU - Liu, Chengfei

AU - Li, Jianxin

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - Most existing studies in community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. However, the bulk of literature ignores the interaction strength among the users in the retrieved communities. As a result, many members of the detected communities do not interact frequently to each other. This inactivity will create problem for online advertisers as they require the community to be highly interactive to efficiently diffuse marketing information. In this paper, we study the problem of detecting attribute-driven active intimate community, that is, for a given input query consisting a set of attributes, we want to find densely-connected communities in which community members actively participate as well as have strong interaction (intimacy) with respect to the given query attributes. We design a novel attribute relevance intimacy score function for the detected communities and establish its desirable properties. To this end, we use an indexed based solution to efficiently discover active intimate communities. Extensive experiments on real data sets show the effectiveness and performance of our proposed method.

AB - Most existing studies in community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. However, the bulk of literature ignores the interaction strength among the users in the retrieved communities. As a result, many members of the detected communities do not interact frequently to each other. This inactivity will create problem for online advertisers as they require the community to be highly interactive to efficiently diffuse marketing information. In this paper, we study the problem of detecting attribute-driven active intimate community, that is, for a given input query consisting a set of attributes, we want to find densely-connected communities in which community members actively participate as well as have strong interaction (intimacy) with respect to the given query attributes. We design a novel attribute relevance intimacy score function for the detected communities and establish its desirable properties. To this end, we use an indexed based solution to efficiently discover active intimate communities. Extensive experiments on real data sets show the effectiveness and performance of our proposed method.

KW - Information diffusion

KW - Intimacy score

KW - Intimate community

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U2 - 10.1007/978-3-319-92013-9_9

DO - 10.1007/978-3-319-92013-9_9

M3 - Conference contribution

AN - SCOPUS:85047915728

SN - 9783319920122

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

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BT - Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings

A2 - Wang, Junhu

A2 - Cong, Gao

A2 - Chen, Jinjun

A2 - Qi, Jianzhong

PB - Springer Verlag

T2 - 29th Australasian Database Conference, ADC 2018

Y2 - 24 May 2018 through 27 May 2018

ER -

Anwar MM, Liu C, Li J. Uncovering attribute-driven active intimate communities. In Wang J, Cong G, Chen J, Qi J, editors, Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings. Springer Verlag. 2018. p. 109-122. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-92013-9_9

Uncovering attribute-driven active intimate communities

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this