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Learning sequential features for cascade outbreak prediction

机译：学习级联爆发预测的顺序特征

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摘要

Information cascades are ubiquitous in various online social networks. Outbreak of cascades could cause huge and unexpected effects. Therefore, predicting the outbreak of cascades at early stage is of vital importance to avoid potential bad effects and take relevant actions. Existing methods either adopt regression or classification technique with exhaustive feature engineering or predict cascade dynamics via modeling the stochastic process of cascades using a hard-coded diffusion-reaction function. One salient issue of these methods is that these methods heavily depend on human-defined knowledge, features or functions. In this paper, we propose to use recurrent neural network with long short-term memory to directly learn sequential patterns from information cascades, working in a fully data-driven manner. With the learned sequential patterns, the outbreak of cascade could be accurately predicted. Extensive experiments on both Twitter and Sina Weibo datasets demonstrate that our method significantly outperforms state-of-the-art methods at the prediction of cascade outbreaks.
机译：信息瀑布在各种在线社交网络中都是无处不在的。瀑布爆发可能导致巨大和意外的影响。因此，预测早期级联的爆发是至关重要的，以避免潜在的不良影响并采取相关行动。现有方法通过使用硬编码扩散反应功能建模级联的随机过程采用穷举特征工程或预测级联动态的回归或分类技术。这些方法的一个突出问题是这些方法大量取决于人类定义的知识，特征或功能。在本文中，我们建议使用具有长短期记忆的经常性神经网络，直接从信息级联学习顺序模式，以完全数据驱动的方式工作。利用所学习的顺序模式，可以准确地预测级联的爆发。关于Twitter和新浪微博数据集的广泛实验表明，我们的方法在预测级联爆发时显着优于最先进的方法。

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  • 来源
    《Knowledge and information systems》 |2018年第3期|共19页
  • 作者

    Gou Chengcheng; Shen Huawei; Du Pan; Wu Dayong; Liu Yue; Cheng Xueqi;

  • 作者单位

    Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci &

    Technol Beijing Peoples R China;

    Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci &

    Technol Beijing Peoples R China;

    Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci &

    Technol Beijing Peoples R China;

    Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci &

    Technol Beijing Peoples R China;

    Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci &

    Technol Beijing Peoples R China;

    Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci &

    Technol Beijing Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动信息理论;
  • 关键词

    Social network; Outbreak prediction; Sequential feature; LSTM; Popularity prediction;

    机译：社交网络;爆发预测;顺序特征;LSTM;人气预测;

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