Overlap Community Detection Based on Node Convergence Degree

Weimin Li, Huai Kou Miao, Qun Jin, Shu Jiang, Qun Jin

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.

    Original languageEnglish
    Title of host publicationProceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages163-167
    Number of pages5
    ISBN (Electronic)9781509040650
    DOIs
    Publication statusPublished - 2016 Oct 11
    Event14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016 - Auckland, New Zealand
    Duration: 2016 Aug 82016 Aug 10

    Other

    Other14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
    CountryNew Zealand
    CityAuckland
    Period16/8/816/8/10

    Fingerprint

    Topology
    Experiments

    Keywords

    • community structure
    • Node convergence degree
    • overlap
    • PageRank

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition
    • Information Systems
    • Computer Science (miscellaneous)
    • Artificial Intelligence
    • Computer Networks and Communications

    Cite this

    Li, W., Miao, H. K., Jin, Q., Jiang, S., & Jin, Q. (2016). Overlap Community Detection Based on Node Convergence Degree. In Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016 (pp. 163-167). [7588840] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46

    Overlap Community Detection Based on Node Convergence Degree. / Li, Weimin; Miao, Huai Kou; Jin, Qun; Jiang, Shu; Jin, Qun.

    Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 163-167 7588840.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Li, W, Miao, HK, Jin, Q, Jiang, S & Jin, Q 2016, Overlap Community Detection Based on Node Convergence Degree. in Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016., 7588840, Institute of Electrical and Electronics Engineers Inc., pp. 163-167, 14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016, Auckland, New Zealand, 16/8/8. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46
    Li W, Miao HK, Jin Q, Jiang S, Jin Q. Overlap Community Detection Based on Node Convergence Degree. In Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 163-167. 7588840 https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46
    Li, Weimin ; Miao, Huai Kou ; Jin, Qun ; Jiang, Shu ; Jin, Qun. / Overlap Community Detection Based on Node Convergence Degree. Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 163-167
    @inproceedings{5c8dbd1c8cee4ebd97ea0a3bf9e15c5c,
    title = "Overlap Community Detection Based on Node Convergence Degree",
    abstract = "Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.",
    keywords = "community structure, Node convergence degree, overlap, PageRank",
    author = "Weimin Li and Miao, {Huai Kou} and Qun Jin and Shu Jiang and Qun Jin",
    year = "2016",
    month = "10",
    day = "11",
    doi = "10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46",
    language = "English",
    pages = "163--167",
    booktitle = "Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",
    address = "United States",

    }

    TY - GEN

    T1 - Overlap Community Detection Based on Node Convergence Degree

    AU - Li, Weimin

    AU - Miao, Huai Kou

    AU - Jin, Qun

    AU - Jiang, Shu

    AU - Jin, Qun

    PY - 2016/10/11

    Y1 - 2016/10/11

    N2 - Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.

    AB - Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.

    KW - community structure

    KW - Node convergence degree

    KW - overlap

    KW - PageRank

    UR - http://www.scopus.com/inward/record.url?scp=84995486473&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84995486473&partnerID=8YFLogxK

    U2 - 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46

    DO - 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46

    M3 - Conference contribution

    AN - SCOPUS:84995486473

    SP - 163

    EP - 167

    BT - Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -