基于K-shell分解与邻居节点度去噪的链路预测方法

链路预测是研究复杂网络结构和演化机制的重要工具，提高链路预测的精度具有重要价值。针对传统的基于网络拓扑结构相似性算法预测精度偏低的问题，从网络优化去噪的角度进行分析，提出了一种基于K-shell分解与邻居节点度（KSDNN）去噪的链路预测方法。该方法首先从全局的角度通过K-shell分解对复杂网络中所有节点进行重要性排序，然后从局部的角度结合节点邻居节点的度对节点重要性进行综合评判，最后对网络数据进行优化后进行链路预测。通过在四个不同的真实网络进行验证，实验结果表明，所提方法预测精度优于K-shell去噪的方法，且相较于传统算法预测精度平均提升了2%左右。

Link prediction method based on K-shell decomposition and neighbor node degree denoising

Link prediction is an important tool to study the structure and evolution mechanism of complex networks, and it is of great value to improve the accuracy of link prediction. To address the problem of low prediction accuracy of traditional network topology similarity-based algorithms, this paper proposed a link prediction method based on K-shell decomposition with neighbor node degree(KSDNN) denoising from the perspective of network optimization denoising. The method firstly ranked the importance of all nodes in a complex network by K-shell decomposition from a global perspective, then made a comprehensive evaluation of node importance from a local perspective by combining the degrees of nodes'' neighbor nodes, and finally performed link prediction after optimizing the network data. Through verification on four different real networks, the experimental results show that the prediction accuracy of the proposed method is better than that of the K-shell denoising method, and the prediction accuracy is improved by about 2% on average compared with the traditional algorithm.