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草:学习从链状级联数据的空间时间性质,用于微观扩散预测.

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    科学领域:

    • 社交网络分析 社交网络分析
    • 信息传播动力学 信息传播动力学
    • 用于社交媒体的机器学习

    背景情况:

    • 信息传播预测对于诸如病毒式营销和人气预测等任务至关重要.
    • 现有的模型因失去时空属性而与级联数据扎,导致"位置跳转"和"分支独立性"问题.
    • 这些问题阻碍了对信息如何通过社交网络传播的准确预测.

    研究的目的:

    • 提出一种新型模型,GRASS (GRU-like注意力单位和结构扩散),用于微观级联预测.
    • 克服当前信息传播模式中"位置跳转"和"行业独立"的局限性.
    • 提高信息传播预测的准确性和区分能力.

    主要方法:

    • 将注意力机制集成到一个封闭的循环单元 (GRU) 中,以扩大受体场并解决"位置跳转".
    • 实施结构性扩散 (SS) 机制以过相关用户并控制级联隐藏状态生成,解决"分支独立性".
    • 使用t分布式随机邻嵌入 (t-SNE) 来可视化隐藏的表示和评估模型的区分能力.

    主要成果:

    • 在hits@κ和map@κ指标上,GRASS模型显著优于最先进的基线模型.
    • 在真实世界数据集上的实验结果证明了GRASS在微观级联预测中的有效性.
    • t-SNE可视化证实,GRASS为不同的级联生成了更具歧视性的潜在表示.

    结论:

    • 拟议的GRASS模型有效地解决了信息传播预测的关键挑战.
    • 与现有方法相比,GRASS提供了更高的准确性和更好的级联动态表示.
    • 该模型对推进社交网络分析和病毒式营销应用的研究具有前景.