2022/07/26 by Yufei Liu, Jie Cao, Liu, Yufei +3
Physics and Astronomy · Psychology · Social Sciences · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mental Health Research Topics #Misinformation and Its Impacts #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2207.13016
openalex publication_date 2022/07/26 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
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.