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 proceedingConference contribution

    1 Citation (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 languageEnglish
    Title of host publicationProceedings - 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
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages177-182
    Number of pages6
    ISBN (Electronic)9781509043149
    DOIs
    Publication statusPublished - 2017 Mar 10
    Event16th IEEE International Conference on Computer and Information Technology, CIT 2016 - Nadi, Fiji
    Duration: 2016 Dec 72016 Dec 10

    Other

    Other16th IEEE International Conference on Computer and Information Technology, CIT 2016
    CountryFiji
    CityNadi
    Period16/12/716/12/10

    Fingerprint

    Electric network analysis
    Experiments

    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

    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 proceedingConference 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
    @inproceedings{8ec7a395b66c423ca635a17e2f86c436,
    title = "Dynamic community mining and tracking based on temporal social network analysis",
    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.",
    keywords = "Community mining, Dynamics tracking, Social network analysis, User correlation",
    author = "Xiaokang Zhou and Wei Liang and Bo Wu and Zixian Lu and Shoji Nishimura and Takashi Shinomiya and Qun Jin",
    year = "2017",
    month = "3",
    day = "10",
    doi = "10.1109/CIT.2016.74",
    language = "English",
    pages = "177--182",
    booktitle = "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.",
    address = "United States",

    }

    TY - GEN

    T1 - Dynamic community mining and tracking based on temporal social network analysis

    AU - Zhou, Xiaokang

    AU - Liang, Wei

    AU - Wu, Bo

    AU - Lu, Zixian

    AU - Nishimura, Shoji

    AU - Shinomiya, Takashi

    AU - Jin, Qun

    PY - 2017/3/10

    Y1 - 2017/3/10

    N2 - 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.

    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.

    KW - Community mining

    KW - Dynamics tracking

    KW - Social network analysis

    KW - User correlation

    UR - http://www.scopus.com/inward/record.url?scp=85017335898&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85017335898&partnerID=8YFLogxK

    U2 - 10.1109/CIT.2016.74

    DO - 10.1109/CIT.2016.74

    M3 - Conference contribution

    SP - 177

    EP - 182

    BT - 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

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -