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    This study introduces new diffusion models and a graph embedding method to solve the influence maximization problem in networks with probabilistically unstable links for multiagent systems.

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    Area of Science:

    • Network Science
    • Multiagent Systems
    • Graph Theory

    Background:

    • The influence maximization problem is crucial for targeted information dissemination.
    • Networks with probabilistically unstable links present unique challenges for traditional influence maximization approaches.
    • Multiagent systems offer a framework for modeling complex network dynamics.

    Purpose of the Study:

    • To develop novel diffusion models for influence maximization in networks with probabilistically unstable links.
    • To establish a multiagent system model for influence maximization considering link instability.
    • To propose a graph embedding method for identifying optimal seed sets in such networks.

    Main Methods:

    • Design of two diffusion models: unstable-link independent cascade (UIC) and unstable-link linear threshold (ULT).
    • Establishment of a multiagent system (MAS) model with interaction rules for probabilistically unstable links (PULs).
    • Development of the unstable-similarity2vec (US2vec) graph embedding approach to capture node structural similarity.

    Main Results:

    • The proposed UIC and ULT models effectively capture diffusion dynamics in networks with PULs.
    • The US2vec method accurately embeds network structures, reflecting node instability.
    • The developed algorithm successfully identifies seed sets for influence maximization using US2vec embeddings.

    Conclusions:

    • The study validates the proposed models and algorithms for influence maximization in networks with PULs.
    • The US2vec approach provides an effective graph embedding solution for identifying optimal seed sets.
    • The findings offer insights into optimizing influence spread in dynamic and uncertain network environments.