As a research hotspot of complex network analysis, link prediction has received growing attention from various disciplines. Link prediction intends to determine the connecting probability of latent links based on the observed structure information. To this end, a host of similarity-based and learning-based link prediction methods have been proposed. To attain stable prediction performance on diverse networks, this paper proposes a supervised similarity-based method, which absorbs the advantages of both kinds of link prediction methods. In the proposed method, to capture the characteristics of a node pair, a collection of structural features is extracted from the network to represent the node pair as a vector. Then, the positive and negative k-nearest neighbors are searched from existing and nonexisting links, respectively. The connection likelihood of a node pair is measured according to its distances to the local mean vectors of positive and negative k-nearest neighbors. The prediction performance of the proposed method is experimentally evaluated on 10 benchmark networks. The results show that the proposed method is superior to the compared methods in terms of accuracy and stableness.

中文翻译：

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基于KNN的链接预测的监督相似性度量

链路预测作为复杂网络分析的研究热点，越来越受到各学科的关注。链接预测旨在根据观察到的结构信息确定潜在链接的连接概率。为此，已经提出了许多基于相似性和基于学习的链接预测方法。为了在不同的网络上获得稳定的预测性能，本文提出了一种基于监督相似性的方法，该方法吸收了两种链路预测方法的优点。在所提出的方法中，为了捕获节点对的特征，从网络中提取结构特征的集合以将节点对表示为向量。然后，正负ķ- 分别从现有和不存在的链接中搜索最近的邻居。节点对的连接可能性是根据其与正负的局部均值向量的距离来衡量的ķ-最近的邻居。该方法的预测性能在 10 个基准网络上进行了实验评估。结果表明，所提出的方法在准确性和稳定性方面均优于对比方法。