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Multiple Influences Maximization Under Dynamic Link Strength in Multi-Agent Systems: The Competitive and Cooperative

Mincan Li, Zidong Wang, Simon J E Taylor

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
    This summary is machine-generated.

    This study introduces a new model for dynamic link strength in multi-agent systems to optimize multiple influences maximization. The proposed distributed deep reinforcement learning framework enhances influence diffusion efficiency in competitive and cooperative settings.

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    Area of Science:

    • Artificial Intelligence
    • Network Science
    • Computer Science

    Background:

    • Multi-agent systems (MASs) present complex challenges in understanding influence propagation.
    • Dynamic link strengths significantly impact diffusion processes, requiring advanced modeling.
    • Optimizing influence spread is crucial for various applications in MASs.

    Purpose of the Study:

    • To propose a novel model for dynamic link strength in MASs to simulate multiple influences diffusion.
    • To formulate the multiple influences maximization under dynamic link strength (MIMDLS) problem considering competitive and cooperative scenarios.
    • To develop a distributed deep reinforcement learning (DRL) framework for efficient seed selection in MIMDLS.

    Main Methods:

    • A novel dynamic link strength model for MASs to simulate multiple influences diffusion.
    • Two diffusion models: competitive multiple influences independent cascade (Cp-MIIC) and cooperative multiple influences linear threshold (Cr-MILT).
    • A distributed DRL framework with asynchronous training and updating for seed selection, including Q-value estimation and constraint management.

    Main Results:

    • Validated the effectiveness and efficiency of the proposed models and algorithms for multiple influence diffusion.
    • Demonstrated superior performance of the developed distributed DRL algorithm compared to state-of-the-art methods.
    • Showcased the capability of the framework in handling both competitive and cooperative influence diffusion scenarios.

    Conclusions:

    • The proposed dynamic link strength model and DRL framework effectively address the MIMDLS problem in MASs.
    • The Cp-MIIC and Cr-MILT models provide robust mechanisms for simulating competitive and cooperative influence diffusion.
    • The distributed DRL approach offers an efficient and scalable solution for optimizing influence maximization in dynamic network environments.