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    This study introduces a hierarchical Bayesian dynamic stochastic block model (HB-DSBM) to capture both node and community dynamics in complex networks. The model effectively detects anomalies and improves community detection in dynamic networks.

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    Area of Science:

    • Network Science
    • Data Mining
    • Statistical Modeling

    Background:

    • Dynamic complex networks exhibit time-varying topological structures.
    • Stochastic Block Models (SBM) are widely used for network analysis but current dynamic extensions focus only on community-level behavior.
    • Existing SBMs for dynamic networks often fail to accurately model individual node dynamics and detect node-level anomalies.

    Purpose of the Study:

    • To propose a novel Hierarchical Bayesian Dynamic Stochastic Block Model (HB-DSBM) for synchronous modeling of node-level and community-level dynamic behaviors.
    • To address the limitations of current community-level dynamic SBMs in temporal community detection and node anomaly detection.
    • To enable the identification of abnormal evolutionary behavior at both node and community levels within dynamic networks.

    Main Methods:

    • Developed a hierarchical Bayesian dynamic SBM (HB-DSBM) incorporating a hierarchical Dirichlet generative mechanism.
    • Integrated global community evolution with microscopic node transition behaviors.
    • Implemented an effective variational inference algorithm for inferring communities and node dynamics.

    Main Results:

    • HB-DSBM achieved state-of-the-art performance in community detection and evolution analysis on simulated and real-world networks.
    • The model successfully models both node-level and community-level dynamic behaviors synchronously.
    • Demonstrated effective identification of abnormal evolutionary behavior and events in dynamic networks.

    Conclusions:

    • The proposed HB-DSBM offers a significant advancement in modeling dynamic complex networks by capturing multi-level dynamics.
    • This approach enhances the accuracy of community detection and provides a robust framework for anomaly detection in dynamic network analysis.
    • HB-DSBM is effective for understanding and identifying unusual patterns in evolving network structures.