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多尺度信息扩散预测与最小替代神经网络的神经网络

Ranran Wang, Xing Xu, Yin Zhang

    IEEE transactions on neural networks and learning systems
    |November 23, 2023
    PubMed
    概括

    这项研究引入了一个新的神经网络,用于预测社交媒体上传播的信息. 该模型准确地预测了整体受欢迎程度和个人用户参与度,考虑了内容竞争和用户预期.

    科学领域:

    • 社交网络分析 社交网络分析
    • 计算社会科学 计算社会科学
    • 信息科学 信息科学 信息科学

    背景情况:

    • 由于社交平台上的内容替代动态,信息传播预测是复杂的.
    • 现有的模型往往忽略了诸如信息间竞争,用户吸引力和内容预测等因素.
    • 预测信息传播需要了解宏观的受欢迎程度和微观的用户级级.

    研究的目的:

    • 提出一种新的神经网络模型,用于多尺度信息扩散预测.
    • 同时解决宏观普及和微观扩散预测任务.
    • 将内容生命周期,用户偏好和预期等因素纳入扩散建模.

    主要方法:

    • 开发了一个使用最小替代 (MIDPMS) 神经网络的多尺度信息扩散预测.
    • 模拟信息传播作为一个替代系统,整合内容生命周期,用户偏好和预期.
    • 利用基于最小替代理论的神经网络进行联合宏观/微观预测培训.

    主要成果:

    • 在宏观和微观预测任务中,MIDPMS模型表现出强的性能.
    • 在微博和推特数据集上的实验验验证了该模型的有效性.
    • 提出的方法成功地捕捉到了信息传播动态的复杂性.

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    结论:

    • 该MIDPMS模型提供了一个强大的解决方案,用于多尺度信息扩散预测.
    • 考虑替换动态和用户预期可以提高预测准确度.
    • 该模型为了解和管理在社交媒体上传播的信息提供了有价值的见解.