MetaLP-DGI: Meta-Learning-Based Link Prediction With Centrality-Aware Deep Graph Infomax Embeddings

Abstract

Link prediction is a fundamental task in social and complex network analysis, focused on forecasting the likelihood of unseen or future connections between nodes. Accurate link prediction can enhance understanding of network dynamics, reveal hidden structures, and improve recommendations in social and information networks. This paper proposes a novel Meta-Learning-Based Link Prediction model that utilizes a Centrality-Aware connectivity matrix and incorporates Deep Graph Infomax (DGI) embeddings with the CatBoost classifier. The connectivity matrix is constructed using node centrality measures like closeness centrality, degree centrality, and betweenness centrality by capturing the network's local and global structural properties. The DGI embedding algorithm efficiently learns the network's latent features, while the CatBoost classifier is employed to enhance prediction performance. To address the challenge of imbalanced datasets in social networks, we apply downsampling to create balanced training and testing datasets, ensuring robust model learning. Our framework demonstrates improved accuracy, scalability, and adaptability compared to traditional link prediction methods. Extensive experiments on real-world social network datasets show that the proposed model achieves superior performance in link prediction tasks, making it a promising approach for various network analysis applications.