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    Area of Science:

    • Network science
    • Data mining
    • Machine learning

    Background:

    • Complex systems often feature intricate networks where direct link observation is challenging.
    • Inaccurate observations from multiple platforms or varying times complicate network link inference.
    • Existing methods struggle with heterogeneous link errors and unknown platform reliability, especially in community-structured networks.

    Purpose of the Study:

    • To address the challenge of link inference in networks with community structures.
    • To develop a method that accounts for unknown platform reliability and heterogeneous link errors.
    • To infer both network links and the reliability of data collection platforms.

    Main Methods:

    • Proposed an expectation maximization algorithm for link inference in network systems with community structures (EMLIC).
    • EMLIC simultaneously infers network links, link errors, and platform observation reliability.
    • Algorithm designed to handle heterogeneous errors sensitive to underlying community structures.

    Main Results:

    • EMLIC demonstrated superior performance in link inference compared to existing algorithms on synthetic and real-world network data.
    • The algorithm successfully inferred platform observation reliability across different communities.
    • Achieved lower link errors, particularly in network data exhibiting community structures.

    Conclusions:

    • The proposed EMLIC algorithm effectively infers network links and platform reliability in complex systems with community structures.
    • EMLIC offers a robust solution for link inference problems with heterogeneous and unreliable data sources.
    • This method advances the analysis of network data in fields like biology and social science.