Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning
This work addresses social influence prediction for applications like marketing and COVID-19 modeling, but it appears incremental as it builds on existing methods like DeepInf.
The authors tackled the problem of predicting social influence, particularly for COVID-19, by extending DeepInf to create DeepPP, a deep learning-based personalized propagation algorithm that combines neural prediction models with page rank analysis, resulting in more accurate predictions compared to baseline methods on four social networks and two COVID-19 datasets.
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.