Link prediction plays an important role in information filtering and numerous research works have been made in this field. However, traditional link prediction algorithms mainly focus on overall prediction accuracy, ignoring the heterogeneity of the prediction accuracy for different links. In this paper, we analyzed the prediction accuracy of each link in networks and found that the prediction accuracy for different links is severely polarized. Further analysis shows that the accuracy of edges with low edge betweenness is consistently high while that of edges with high edge betweenness is consistently low, i.e. AUC follows a bimodal distribution with one peak around 0.5 and the other peak around 1. Our results indicate that link prediction algorithms should focus more on edges with high betweenness instead of edges with low betweenness. To improve the accuracy of edges with high betweenness, we proposed an improved algorithm called RA_LP which takes advantage of resource transfer of the second-order and third-order paths of local path. Results show that this algorithm can improve the link prediction accuracy for edges with high betweenness as well as the overall accuracy.

链接预测在信息过滤中发挥着重要作用，该领域已经做出了大量的研究工作。然而，传统的链路预测算法主要关注整体预测精度，忽略了不同链路的预测精度的异质性。本文分析了网络中各个链路的预测精度，发现不同链路的预测精度两极分化严重。进一步分析表明，具有低边缘介数的边缘的准确性始终较高，而具有高边缘介数的边缘的准确性始终较低，即 AUC 遵循双峰分布，一个峰值约为 0.5，另一个峰值约为 1。我们的结果表明，链接预测算法应该更多地关注介数高的边缘，而不是介数低的边缘。为了提高高介数边的精度，我们提出了一种名为RA_LP的改进算法，该算法利用了局部路径的二阶和三阶路径的资源转移。结果表明，该算法能够提高高介数边的链路预测精度以及整体精度。