Analysis of User Network and Correlation for Community Discovery Based on Topic-Aware Similarity and Behavioral Influence

Xiaokang Zhou, Bo Wu, Qun Jin

    Research output: Contribution to journalArticlepeer-review

    36 Citations (Scopus)


    While social computing related research has focused mostly on how to provide users with more precise and direct information, or on recommending new search methods to find requested information rapidly, the authors believe that network users themselves could be viewed as an important social resource. This study concentrates on analyzing potential and dynamic user correlations, based on topic-aware similarity and behavioral influence, which may help us to discover communities in social networking sites. The dynamically socialized user networking (DSUN) model is extended and refined to represent implicit and explicit user relationships in terms of topic-aware features and social behaviors. A set of measures is defined to describe and quantify interuser correlations, relating to social behaviors. Three types of ties are proposed to describe and discover communities according to influence-based user relationships. Results of the experiment with Twitter data are used to show the discovery of three types of communities, based on the presented model. Comparison with six different schemes and two existing methods demonstrates that the proposed method is effective in discovering influence-based communities. Finally, the scenario-based simulation of collective decision-making processes demonstrates the practicability of the proposed model and method in social interactive systems.

    Original languageEnglish
    JournalIEEE Transactions on Human-Machine Systems
    Publication statusAccepted/In press - 2017 Aug 29


    • Analytical models
    • Community discovery
    • Correlation
    • Data mining
    • Feature extraction
    • Heuristic algorithms
    • social behavior analysis
    • social influence
    • social network analysis
    • Twitter
    • user correlation

    ASJC Scopus subject areas

    • Human Factors and Ergonomics
    • Control and Systems Engineering
    • Signal Processing
    • Human-Computer Interaction
    • Computer Science Applications
    • Computer Networks and Communications
    • Artificial Intelligence

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