Link prediction is a fundamental challenge in network science. Among various methods, similarity-based algorithms are popular for its simplicity, interpretability, high efficiency and satisfactory performance. In this Letter , we show that the most elementary index, namely the common neighbor (CN) index, is dominated by the leading eigenvector of the adjacency matrix of the target network. Accordingly, we propose a parameter-free algorithm that keeps the contributions of the leading eigenvector and the secondary eigenvector the same. Extensive experiments on real networks demonstrate that the prediction performance of the proposed algorithm is remarkably better than well-performed local similarity indices in the literature. A further proposed algorithm that can adjust the contribution of leading eigenvector shows the superiority over state-of-the-art global algorithms for both higher accuracy and lower computational complexity.
Link prediction is a fundamental challenge in network science. Among various methods, similarity-based algorithms are popular for its simplicity, interpretability, high efficiency and satisfactory performance. In this paper , we show that the most elementary index, namely the common neighbor (CN) index, is dominated by the leading eigenvector of the adjacency matrix of the target network. Accordingly, we propose a parameter-free algorithm that ensures the contributions of the leading eigenvector and the secondary eigenvector the same. Extensive experiments on real networks demonstrate that the prediction performance of the proposed algorithm is remarkably better than well-performed local similarity indices in the literature. A further proposed algorithm that can adjust the contribution of leading eigenvector shows the superiority over state-of-the-art algorithms with tunable parameters for its competitive accuracy and lower computational complexity.
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