Dynamic community mining and tracking based on temporal social network analysis

Xiaokang Zhou; Wei Liang; Bo Wu; Zixian Lu; Shoji Nishimura; Takashi Shinomiya; Qun Jin

doi:10.1109/CIT.2016.74

Dynamic community mining and tracking based on temporal social network analysis

Xiaokang Zhou, Wei Liang, Bo Wu, Zixian Lu, Shoji Nishimura, Takashi Shinomiya, Qun Jin

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

3 Citations (Scopus)

Abstract

Nowadays, the analysis of social networks, as well as the community evolution has become a hotly discussed topic in social computing field. In this paper, we focus on mining and tracking the dynamic communities based on social networking analysis. Based on a generic framework for the dynamic community discovery, a computational approach is developed to extract users' static and dynamic features for the temporal trend detection. A dynamically socialized user networking model is then presented to describe users' various social relationships. A mechanism is proposed and developed to detect the dynamic user communities, and track their evolving changes. Experiments using Twitter data demonstrate the effectiveness of our method in tracking how communities dynamically create, split, and merge from a group of connected people in social media environments.

Original language	English
Title of host publication	Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	177-182
Number of pages	6
ISBN (Electronic)	9781509043149
DOIs	https://doi.org/10.1109/CIT.2016.74
Publication status	Published - 2017 Mar 10
Event	16th IEEE International Conference on Computer and Information Technology, CIT 2016 - Nadi, Fiji Duration: 2016 Dec 7 → 2016 Dec 10

Other

Other	16th IEEE International Conference on Computer and Information Technology, CIT 2016
Country/Territory	Fiji
City	Nadi
Period	16/12/7 → 16/12/10

Keywords

Community mining
Dynamics tracking
Social network analysis
User correlation

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Information Systems
Safety, Risk, Reliability and Quality

Access to Document

10.1109/CIT.2016.74

Cite this

Zhou, X., Liang, W., Wu, B., Lu, Z., Nishimura, S., Shinomiya, T., & Jin, Q. (2017). Dynamic community mining and tracking based on temporal social network analysis. In Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016 (pp. 177-182). [7876335] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIT.2016.74

Dynamic community mining and tracking based on temporal social network analysis. / Zhou, Xiaokang; Liang, Wei; Wu, Bo; Lu, Zixian; Nishimura, Shoji; Shinomiya, Takashi; Jin, Qun.

Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 177-182 7876335.

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

Zhou, X, Liang, W, Wu, B, Lu, Z, Nishimura, S, Shinomiya, T & Jin, Q 2017, Dynamic community mining and tracking based on temporal social network analysis. in Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016., 7876335, Institute of Electrical and Electronics Engineers Inc., pp. 177-182, 16th IEEE International Conference on Computer and Information Technology, CIT 2016, Nadi, Fiji, 16/12/7. https://doi.org/10.1109/CIT.2016.74

Zhou X, Liang W, Wu B, Lu Z, Nishimura S, Shinomiya T et al. Dynamic community mining and tracking based on temporal social network analysis. In Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 177-182. 7876335 https://doi.org/10.1109/CIT.2016.74

Zhou, Xiaokang ; Liang, Wei ; Wu, Bo ; Lu, Zixian ; Nishimura, Shoji ; Shinomiya, Takashi ; Jin, Qun. / Dynamic community mining and tracking based on temporal social network analysis. Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 177-182

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AB - Nowadays, the analysis of social networks, as well as the community evolution has become a hotly discussed topic in social computing field. In this paper, we focus on mining and tracking the dynamic communities based on social networking analysis. Based on a generic framework for the dynamic community discovery, a computational approach is developed to extract users' static and dynamic features for the temporal trend detection. A dynamically socialized user networking model is then presented to describe users' various social relationships. A mechanism is proposed and developed to detect the dynamic user communities, and track their evolving changes. Experiments using Twitter data demonstrate the effectiveness of our method in tracking how communities dynamically create, split, and merge from a group of connected people in social media environments.

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Dynamic community mining and tracking based on temporal social network analysis

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Keywords

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