Link Prediction by Combining Local Structure Similarity With Node Behavior Synchronization

Link prediction plays a crucial role in discovering missing information and understanding evolutionary mechanisms in complex networks, so several algorithms have been proposed. However, existing link prediction algorithms usually rely only on structural information, limiting the potential for further accuracy improvement. Recently, the significance of node behavior synchronization in network reconstruction has emerged. Both link prediction and network reconfiguration aim to reveal the underlying network structure, so node behavior synchronization has the potential to improve link prediction accuracy. In this study, we propose a mutual information-based method to quantitatively measure node behavior synchronization, which is more suitable for link prediction and yields more stable performance than the methods based on node behavior's temporal similarity. Further, we propose a link prediction algorithm that combines local structural similarity with node behavior synchronization. Experimental results on real-life networks show that the proposed method is competitive in accuracy compared to methods relying solely on network structure or exploiting information about node behavior. In addition, the analysis of the prediction performance with different combination ratios reveals the role of node behavior synchronization in different types of real networks. Our study not only improves the performance of link prediction, but also helps to reveal the role of node behavior synchronization in different types of networks.