Discovering and tracking active online social groups

Md Musfique Anwar, Chengfei Liu, Jianxin Li, Tarique Anwar

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

Most existing works on detection of social groups or communities in online social networks consider only the common topical interest of users as the basis for grouping. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on two real data sets to demonstrate the effectiveness and performance of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups.
Original languageEnglish
Title of host publicationWeb Information Systems Engineering - WISE 2017
Subtitle of host publication18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I
EditorsA. Bouguettaya, Y. Gao, A. Klimenko, L. Chen, X. Zhang, F. Dzerzhinskiy, W. Jia, S.V. Klimenko, Q. Li
Place of PublicationCham, Switzerland
PublisherSpringer
Pages59-74
Number of pages16
ISBN (Electronic)9783319687834
ISBN (Print)9783319687827
DOIs
Publication statusPublished - 2017
Event18th International Conference on Web Information Systems Engineering - Puschino, Russian Federation
Duration: 7 Oct 201711 Oct 2017
Conference number: 200069

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10569
ISSN (Print)0302-9743

Conference

Conference18th International Conference on Web Information Systems Engineering
Abbreviated titleWISE 2017
CountryRussian Federation
CityPuschino
Period7/10/1711/10/17

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Cite this

Anwar, M. M., Liu, C., Li, J., & Anwar, T. (2017). Discovering and tracking active online social groups. In A. Bouguettaya, Y. Gao, A. Klimenko, L. Chen, X. Zhang, F. Dzerzhinskiy, W. Jia, S. V. Klimenko, ... Q. Li (Eds.), Web Information Systems Engineering - WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I (pp. 59-74). (Lecture Notes in Computer Science ; Vol. 10569). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-68783-4_5
Anwar, Md Musfique ; Liu, Chengfei ; Li, Jianxin ; Anwar, Tarique. / Discovering and tracking active online social groups. Web Information Systems Engineering - WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I. editor / A. Bouguettaya ; Y. Gao ; A. Klimenko ; L. Chen ; X. Zhang ; F. Dzerzhinskiy ; W. Jia ; S.V. Klimenko ; Q. Li. Cham, Switzerland : Springer, 2017. pp. 59-74 (Lecture Notes in Computer Science ).
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abstract = "Most existing works on detection of social groups or communities in online social networks consider only the common topical interest of users as the basis for grouping. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on two real data sets to demonstrate the effectiveness and performance of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups.",
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Anwar, MM, Liu, C, Li, J & Anwar, T 2017, Discovering and tracking active online social groups. in A Bouguettaya, Y Gao, A Klimenko, L Chen, X Zhang, F Dzerzhinskiy, W Jia, SV Klimenko & Q Li (eds), Web Information Systems Engineering - WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I. Lecture Notes in Computer Science , vol. 10569, Springer, Cham, Switzerland, pp. 59-74, 18th International Conference on Web Information Systems Engineering, Puschino, Russian Federation, 7/10/17. https://doi.org/10.1007/978-3-319-68783-4_5

Discovering and tracking active online social groups. / Anwar, Md Musfique; Liu, Chengfei; Li, Jianxin; Anwar, Tarique.

Web Information Systems Engineering - WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I. ed. / A. Bouguettaya; Y. Gao; A. Klimenko; L. Chen; X. Zhang; F. Dzerzhinskiy; W. Jia; S.V. Klimenko; Q. Li. Cham, Switzerland : Springer, 2017. p. 59-74 (Lecture Notes in Computer Science ; Vol. 10569).

Research output: Chapter in Book/Conference paperConference paper

TY - GEN

T1 - Discovering and tracking active online social groups

AU - Anwar, Md Musfique

AU - Liu, Chengfei

AU - Li, Jianxin

AU - Anwar, Tarique

PY - 2017

Y1 - 2017

N2 - Most existing works on detection of social groups or communities in online social networks consider only the common topical interest of users as the basis for grouping. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on two real data sets to demonstrate the effectiveness and performance of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups.

AB - Most existing works on detection of social groups or communities in online social networks consider only the common topical interest of users as the basis for grouping. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on two real data sets to demonstrate the effectiveness and performance of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups.

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DO - 10.1007/978-3-319-68783-4_5

M3 - Conference paper

SN - 9783319687827

T3 - Lecture Notes in Computer Science

SP - 59

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BT - Web Information Systems Engineering - WISE 2017

A2 - Bouguettaya, A.

A2 - Gao, Y.

A2 - Klimenko, A.

A2 - Chen, L.

A2 - Zhang, X.

A2 - Dzerzhinskiy, F.

A2 - Jia, W.

A2 - Klimenko, S.V.

A2 - Li, Q.

PB - Springer

CY - Cham, Switzerland

ER -

Anwar MM, Liu C, Li J, Anwar T. Discovering and tracking active online social groups. In Bouguettaya A, Gao Y, Klimenko A, Chen L, Zhang X, Dzerzhinskiy F, Jia W, Klimenko SV, Li Q, editors, Web Information Systems Engineering - WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I. Cham, Switzerland: Springer. 2017. p. 59-74. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-319-68783-4_5