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    This study optimizes observer sets for diffusion-source inference (DSI) to precisely target information or disease origins. The novel percolation-based evolutionary framework (PrEF) minimizes candidate sets for more effective source identification.

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    Area of Science:

    • Network Science
    • Information Science
    • Computational Social Science

    Background:

    • The diffusion-source inference (DSI) problem is crucial for applications like combating misinformation and controlling disease spread.
    • Optimizing DSI requires precise source targeting, but conventional metrics fail in heterogeneous networks.

    Purpose of the Study:

    • To develop a method for optimizing observer set configurations to improve DSI accuracy.
    • To introduce a new metric, the candidate set, for evaluating DSI effectiveness.
    • To propose a framework for analyzing DSI in large-scale networks.

    Main Methods:

    • Proposed the percolation-based evolutionary framework (PrEF) to optimize observer sets.
    • Introduced the candidate set metric, bounded by the largest component cover.
    • Leveraged network percolation and evolutionary algorithms, drawing parallels with network immunization.

    Main Results:

    • PrEF significantly minimizes candidate sets compared to state-of-the-art methods across diverse networks.
    • The approach demonstrates superior performance in 26/27 empirical networks and 155/162 critical threshold cases.
    • The method is robust across varied infection probabilities, diffusion models, and network structures.

    Conclusions:

    • The PrEF framework offers a more effective and stable solution for the DSI problem.
    • This work provides a valuable tool for analyzing and improving source inference in complex networks.
    • The proposed candidate set metric offers a more general evaluation of DSI approaches.