Common Neighbor Completion with Information Entropy for Link Prediction in Social Networks

• DOI: 10.1007/s41019-024-00267-6
• Publication Date: 2025-01-24T07:53:23Z
• Authors: Zhengyun Zhou; Guojia Wan; Bo Du
• Keywords: Link (geometry); Link analysis

Abstract Link prediction is essential for identifying hidden relationships within network data, with significant implications fields such as social analysis and bioinformatics. Traditional methods often overlook potential among common neighbors, limiting their effectiveness in utilizing graph information fully. To address this, we introduce a novel approach, Common Neighbor Completion Information Entropy (IECNC), which enhances model expressiveness by considering logical neighbor relationships. Our method integrates dynamic node function Message Passing Neural Network (MPNN), focusing on first-order neighbors employing set-based aggregation to improve missing link predictions. By combining the entropy of probabilistic predictions MPNN leveraging assess uncertainty adjacent connections, our approach significantly accuracy. Experimental results demonstrate that IECNC achieves optimal performance across multiple datasets, surpassing existing techniques. Furthermore, visualizations confirm effectively captures accurately learns feature from various categories, Demonstrating method’s efficacy adaptability.