Advancing Link Prediction with a Hybrid Graph Neural Network Approach

Social media platforms produce extensive user–item interaction data that demand advanced analytical models for effective personalization. This study investigates the link prediction task within social recommendation systems using Graph Neural Networks (GNNs). A hybrid framework is proposed that integrates Graph Convolutional Networks (GCNs) with dual similarity metrics combining cosine and dot product measures to enhance link prediction accuracy. Experiments conducted on the Ciao and Epinions datasets using the Graph Convolutional Network (GCN) demonstrate superior performance compared with baseline models such as GraphRec and GraphSAGE. The proposed approach effectively captures latent interaction patterns, providing a robust foundation for more accurate and personalized recommendation systems on social media platforms.