论文标题
通过深入学习对Covid-19的社会影响进行建模
Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning
论文作者
论文摘要
社会影响力预测渗透到许多领域，包括营销，行为预测，推荐系统等。但是，预测社会影响力的传统方法不仅需要领域专业知识，而且还依靠提取用户功能，这可能非常乏味。此外，处理非欧几里得空间中的图形数据的图形卷积网络（GCN）并不直接适用于欧几里得空间。为了克服这些问题，我们扩展了DeepInf，以便它可以通过页面排名域的过渡概率来预测Covid-19的社会影响。此外，我们的实施产生了一种基于深度学习的个性化繁殖算法，称为DEEPPP。所得算法将神经预测模型的个性化传播与来自页面级分析的神经预测模型的近似个性化传播相结合。来自不同领域的四个社交网络以及两个COVID-19数据集用于证明所提出算法的效率和有效性。与其他基线方法相比，DEEPPP提供了更准确的社会影响预测。此外，实验表明DEEPPP可以应用于Covid-19的现实世界预测数据。
Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise,they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to demonstrate the efficiency and effectiveness of the proposed algorithm. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.
