Link prediction is one of the most important and challenging tasks in complex network analysis, which aims to predict missing link based on existing ones in a network. This problem is of both theoretical interest and has applications in diverse scientific disciplines, including social network analysis, recommendation systems, and biological networks. In this paper we propose a novel link prediction method that aims at improving the accuracy of existing path-based methods by incorporating information about the nodes along local paths. We investigate the proposed framework empirically and conduct extensive experiments on real-world datasets obtained from diverse domains. Results show that the proposed method has achieved increased prediction accuracy when compared to existing state-of-the-art link prediction methods.

链路预测是复杂网络分析中最重要和最具挑战性的任务之一，旨在基于网络中的现有链路来预测丢失的链路。这个问题既有理论意义，又在包括社会网络分析，推荐系统和生物网络在内的多种科学学科中得到应用。在本文中，我们提出了一种新颖的链接预测方法，旨在通过合并有关沿局部路径的节点的信息来提高现有基于路径的方法的准确性。我们将凭经验调查提议的框架，并对从不同领域获得的真实数据集进行广泛的实验。结果表明，与现有的最新链路预测方法相比，该方法已提高了预测精度。