Abstract The current similarity-based link prediction methods ignore the ability of the optimal path to transfer similarity between nodes. To solve this problem, a link prediction method based on the optimal path similarity transmission matrix is proposed. Firstly, the influence of the optimal path between nodes on the information transmission capacity is analyzed, then the tight centrality between nodes is defined; secondly, the number of optimal paths and centrality is used to construct the similarity transmission matrix, and the local information between nodes and global attributes are integrated to evaluate the similarity between nodes. Finally, the proposed method is compared with other similarity-based algorithms in six real networks. The results show that the proposed algorithm has more accurate prediction accuracy and is more stable.
LI Qiaoli,HAN Hua,LI Qiuhui, et al. Link Prediction Method Based on Optimal Path Similarity Transfer Matrix[J]. Complex Systems and Complexity Science,
2023, 20(1): 9-17.
