Modeling user success in online social networks using advanced GNN architectures

Abstract

Online social networks (OSNs) provide extensive data reflecting users’ personalities, interests, and social connections. The study explores how graph convolutional neural networks (GCNNs) can be used to analyze data from the VKontakte social network to predict users' professional success. Using features like user profiles and social connections, it evaluates various GCNN architectures, including GCNConv, SAGEConv, and GINConv. The Graph Isomorphism Network (GIN) layer achieved the highest accuracy (0.88). This research highlights the effectiveness of advanced neural networks in understanding professional success metrics in online social networks. Hong Bang International University, Ho Chi Minh City, Vietnam.