Persistence Augmented Graph Convolution Network for Information Popularity Prediction

In recent years, information popularity prediction of online social media plays a vital role in crisis early warning and malicious content propagation identification within public opinion management application scenarios. Existing work lacks effective mechanisms for interactive topology feature extraction among multiple correlated cascades, which contributes the propagation scale prediction especially at the early propagation stage. In this paper, we propose a Persistence augmented Graph Convolution Network framework (PT-GCN) to make popularity prediction defined as retweet number of information propagation. Persistence as the topological data analysis approach is utilized to measure and find out the salient topology structure feature. Based on it, we propose to use multi-dimensional cascade graphs to model the correlated cascades and then execute PT-GCN to form the interactive propagation features from correlated nodes in propagation and correlated cascades with persistence & contents. The performance evaluations are based on three datasets from Weibo and Twitter platform. By comparisons with the other related work, PT-GCN achieves much better efficiency in terms of MSLE and precision, which performs well for early stage propagation.