Predicting Virality on Networks Using Local Graphlet Frequency Distribution
Predicting Virality on Networks Using Local Graphlet Frequency Distribution
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DOI：
10.1109/bigdata.2018.8622605
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Andre Baas;Frances Hung;Hao Sha;M. Hasan;G. Mohler
中科院分区：
文献类型：
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作者：
Andre Baas;Frances Hung;Hao Sha;M. Hasan;G. Mohler
预测病毒式传播的任务具有深远的影响，从广告界到最近减少假新闻传播的尝试。之前的工作表明，图基分布是预测病毒式传播的有效特征。在这里，我们研究了使用源节点周围聚合的以边缘为中心的局部图作为病毒式传播预测的特征。这些预测特征用于预测与时间无关的霍克斯模型和病毒式独立级联模型的预期病毒式传播。在 Hawkes 模型中，我们使用线性回归来预测 Hawkes 事件的数量和节点排名，而在独立级联模型中，我们使用逻辑回归来预测 k 大小的级联的大小是否会乘以因子 X。我们的研究表明，局部图频率分布可以有效捕获霍克斯过程和独立级联过程模拟的病毒过程的方差。此外，我们还确定了一组可能在病毒传播过程中发挥重要作用的局部图谱。我们将我们的方法的有效性与基于特征向量中心性的节点选择进行比较。
The task of predicting virality has far-reaching consequences, from the world of advertising to more recent attempts to reduce the spread of fake news. Previous work has shown that graphlet distribution is an effective feature for predicting virality. Here, we investigate the use of aggregated edge-centric local graphlets around source nodes as features for virality prediction. These prediction features are used to predict expected virality for both a time-independent Hawkes model and an independent cascade model of virality. In the Hawkes model, we use linear regression to predict the number of Hawkes events and node ranking, while in the independent cascade model we use logistic regression to predict whether a k-size cascade will multiply by a factor X in size. Our study indicates that local graphlet frequency distribution can effectively capture the variances of the viral processes simulated by Hawkes process and independent-cascade process. Furthermore, we identify a group of local graphlets which might be significant in the viral processes. We compare the effectiveness of our methods with eigenvector centrality-based node choice.