Year: 2025
Type: article
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
Source: Tạp chí Khoa học Lạc Hồng
Institutions: Hong Bang International University, Vinh University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410605252