MRAInf: Multilayer Relation Attention based Social Influence Prediction Net with Local Stimulation

Abstract

Social networks have been part of human beings' daily lives and affect nearly every aspect of our lives. Social influence prediction is an interesting topic to predict whether users will or will not be activated by current social spreading events, and deep learning-based approaches can obtain outstanding accuracy by graph neural networks (GNNs). However, GNN models are restricted by the 1-Weisfeiler-Lehman (WL) test and represent the node structure by only the first layer neighbors but cannot discriminate nodes with different second or more layer neighbors. Therefore, we propose a multilayer relation attention-based social influence prediction Net using GAT with local stimulation, named after MRAInf. Specifically, we design an enhanced node representation (ENR) to describe the original node structure vector by three-layer-neighbor adjacency relations, with more details for attention in GAT. Moreover, in GAT, we design a local stimulation (LS) mechanism with multiple 1D convolutions to reinforce the feature map of the target node being predicted and to weaken the feature maps of non-target nodes. Detailed latent information on ENR and local stimulation for targets in GAT benefit social influence prediction. We conduct extensive experiments on three benchmark datasets: Twitter, Open Academic Graph, and Digg, and the experimental results show that our approach outperforms existing comparison methods in terms of classification performance and predictive accuracy.