Advancing Link Prediction with a Hybrid Graph Neural Network Approach

Siwar Gharsallah, Samah Yahia, Wided Bouchelligua () and Tahani Bouchrika
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Siwar Gharsallah: Research Team in Intelligent Machines (RTIM), National Engineering School of Gabes (ENIG), University of Gabes, Zrig, Gabes 6029, Tunisia
Samah Yahia: Research Laboratory Modeling, Analysis and Control of Systems (MACS), National Engineering School of Gabes (ENIG), Zrig, Gabes 6029, Tunisia
Wided Bouchelligua: Applied College, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Tahani Bouchrika: Computer Science Department, University College of Haql, University of Tabuk, Tabuk 47512, Saudi Arabia

Mathematics, 2025, vol. 13, issue 22, 1-20

Abstract: 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.

Keywords: graph neural networks; graph convolutional network; link prediction; similarity metrics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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