Network-Structure Similarity using Matrix Forest Index, MapSim and Adaptive Path-based LinkGyp with Graph Structural Embeddings for Neonatal Protein Interaction Prediction - Singh, Nirmaljit; Kaur, Nirmal (Journal of Neonatal Surgery, 2025)
Link prediction in complex networks is a critical challenge with wide-ranging applications in biological, social, and technological systems. We propose NSMFIMAP (Network-Structure Similarity using Matrix Forest Index, MapSim, and Adaptive Path-based LinkGyp with Graph Structural Embeddings), an advanced hybrid framework that integrates four key approaches: (1) a regularized Weighted Matrix Forest Index (WPMFI) for global connectivity, (2) hierarchical community detection via MapSim, (3) degree-adaptive path scoring through LinkGYP, and (4) spectral Graph Structural Embeddings for latent role identification. This unified model achieves high predictive accuracy by combining multi-scale network features through optimized eigenvector decomposition, information-theoretic compression, and kernel-based similarity fusion. Applied to neonatal protein-protein interaction (PPI) networks (NSMFIMAP-Med), it demonstrates strong performance in predicting unknown interactions related to developmental disorders and preterm birth complications while maintaining computational efficiency. The framework identifies novel pathways for key gene clusters, offering a scalable, interpretable tool for biomarker discovery and therapeutic target identification in neonatal medicine