BSSLP: A zero-similarity resolving link prediction method combining base and structural similarities in complex networks

This paper presents a novel link prediction approach, termed Basic-Structural Similarity Link Prediction (BSSLP), designed to address the zero-similarity problem in complex networks. BSSLP integrates basic similarity, which establishes a nonzero baseline for all node pairs, with structural similarity that captures both local and intermediate topological features. This integration effectively mitigates challenges such as cold start and sparse network prediction. Through extensive experiments on nine real-world networks, BSSLP consistently outperforms seven benchmark methods, achieving an average AUC improvement of 5.17%. The method demonstrates robust performance across various network structures and maintains high prediction accuracy under different training set proportions. By providing nonzero similarity estimates for all potential edges, BSSLP significantly enhances the prediction of new connections and offers deeper insights into network dynamics.