Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning

TL;DR

This paper introduces DeepPP, a deep learning-based personalized propagation algorithm that predicts COVID-19 social influence more accurately by combining neural prediction with page rank analysis, applicable to real-world data.

Contribution

The paper extends DeepInf to predict COVID-19 social influence using a novel personalized propagation algorithm called DeepPP, integrating neural models with page rank analysis.

Findings

DeepPP outperforms baseline methods in accuracy.

Effective on multiple social networks and COVID-19 datasets.

Demonstrates applicability to real-world COVID-19 data.

Abstract

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.