Detection of temporal communities in mobile social networks

Mengni Ruan; Huan Zhou; Dawei Li; Xuxun Liu; Qingyong Deng

doi:10.1109/INFOCOMWKSHPS50562.2020.9162875

Detection of temporal communities in mobile social networks

Mengni Ruan, Huan Zhou, Dawei Li, Xuxun Liu, Qingyong Deng

Computer Science

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

1 Scopus citations

Abstract

In recent years, community detection in Mobile Social Networks (MSNs) has attracted a lot of attentions from both academia and industry areas. The existing methods of community detection usually focus on static networks. However, MSNs in essence are dynamically intermittently connected networks with a time-varying topology. Therefore, this paper proposes a novel temporal community detection method based on nodes' temporal closeness. In order to evaluate the performance of the proposed method and study the evolution law of the temporal community, we conduct extensive simulations using two real mobility traces, the MIT Reality trace and the Infocom 06 trace. The results show that many temporal communities share similar members and appear at different times. Furthermore, we find the periodic appearance of temporal community.

Original language	English
Title of host publication	IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1021-1026
Number of pages	6
ISBN (Electronic)	9781728186955
DOIs	https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162875
State	Published - Jul 2020
Event	2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 - Toronto, Canada Duration: 6 Jul 2020 → 9 Jul 2020

Publication series

Name	IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

Conference

Conference	2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
Country/Territory	Canada
City	Toronto
Period	6/07/20 → 9/07/20

Keywords

Community Similarity
Mobile Social Networks
Network Model
Temporal Closeness
Temporal Community Detection

Access to Document

10.1109/INFOCOMWKSHPS50562.2020.9162875

Cite this

Ruan, M., Zhou, H., Li, D., Liu, X., & Deng, Q. (2020). Detection of temporal communities in mobile social networks. In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 (pp. 1021-1026). Article 9162875 (IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162875

Ruan, Mengni ; Zhou, Huan ; Li, Dawei et al. / Detection of temporal communities in mobile social networks. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1021-1026 (IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020).

@inproceedings{5e14b36c83f84964bf812c580134f3d9,

title = "Detection of temporal communities in mobile social networks",

abstract = "In recent years, community detection in Mobile Social Networks (MSNs) has attracted a lot of attentions from both academia and industry areas. The existing methods of community detection usually focus on static networks. However, MSNs in essence are dynamically intermittently connected networks with a time-varying topology. Therefore, this paper proposes a novel temporal community detection method based on nodes' temporal closeness. In order to evaluate the performance of the proposed method and study the evolution law of the temporal community, we conduct extensive simulations using two real mobility traces, the MIT Reality trace and the Infocom 06 trace. The results show that many temporal communities share similar members and appear at different times. Furthermore, we find the periodic appearance of temporal community.",

keywords = "Community Similarity, Mobile Social Networks, Network Model, Temporal Closeness, Temporal Community Detection",

author = "Mengni Ruan and Huan Zhou and Dawei Li and Xuxun Liu and Qingyong Deng",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 ; Conference date: 06-07-2020 Through 09-07-2020",

year = "2020",

month = jul,

doi = "10.1109/INFOCOMWKSHPS50562.2020.9162875",

language = "English",

series = "IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1021--1026",

booktitle = "IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020",

}

Ruan, M, Zhou, H, Li, D, Liu, X & Deng, Q 2020, Detection of temporal communities in mobile social networks. in IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020., 9162875, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1021-1026, 2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020, Toronto, Canada, 6/07/20. https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162875

Detection of temporal communities in mobile social networks. / Ruan, Mengni; Zhou, Huan; Li, Dawei et al.
IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1021-1026 9162875 (IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020).

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

TY - GEN

T1 - Detection of temporal communities in mobile social networks

AU - Ruan, Mengni

AU - Zhou, Huan

AU - Li, Dawei

AU - Liu, Xuxun

AU - Deng, Qingyong

PY - 2020/7

Y1 - 2020/7

N2 - In recent years, community detection in Mobile Social Networks (MSNs) has attracted a lot of attentions from both academia and industry areas. The existing methods of community detection usually focus on static networks. However, MSNs in essence are dynamically intermittently connected networks with a time-varying topology. Therefore, this paper proposes a novel temporal community detection method based on nodes' temporal closeness. In order to evaluate the performance of the proposed method and study the evolution law of the temporal community, we conduct extensive simulations using two real mobility traces, the MIT Reality trace and the Infocom 06 trace. The results show that many temporal communities share similar members and appear at different times. Furthermore, we find the periodic appearance of temporal community.

AB - In recent years, community detection in Mobile Social Networks (MSNs) has attracted a lot of attentions from both academia and industry areas. The existing methods of community detection usually focus on static networks. However, MSNs in essence are dynamically intermittently connected networks with a time-varying topology. Therefore, this paper proposes a novel temporal community detection method based on nodes' temporal closeness. In order to evaluate the performance of the proposed method and study the evolution law of the temporal community, we conduct extensive simulations using two real mobility traces, the MIT Reality trace and the Infocom 06 trace. The results show that many temporal communities share similar members and appear at different times. Furthermore, we find the periodic appearance of temporal community.

KW - Community Similarity

KW - Mobile Social Networks

KW - Network Model

KW - Temporal Closeness

KW - Temporal Community Detection

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U2 - 10.1109/INFOCOMWKSHPS50562.2020.9162875

DO - 10.1109/INFOCOMWKSHPS50562.2020.9162875

M3 - Conference contribution

AN - SCOPUS:85091494186

T3 - IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

SP - 1021

EP - 1026

BT - IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

Y2 - 6 July 2020 through 9 July 2020

ER -

Ruan M, Zhou H, Li D, Liu X, Deng Q. Detection of temporal communities in mobile social networks. In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1021-1026. 9162875. (IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020). doi: 10.1109/INFOCOMWKSHPS50562.2020.9162875

Detection of temporal communities in mobile social networks

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