Li Shibao, Huang Junwei, Zhang Zhigang, Liu Jianhang, Huang Tingpei, Chen Haihua
China University of Petroleum, College of Computer and Communication Engineering, Qingdao, Shandong, 266580, China.
Sci Rep. 2018 Nov 19;8(1):17014. doi: 10.1038/s41598-018-35423-2.
Link prediction aims to predict the existence of unknown links via the network information. However, most similarity-based algorithms only utilize the current common neighbor information and cannot get high enough prediction accuracy in evolving networks. So this paper firstly defines the future common neighbors that can turn into the common neighbors in the future. To analyse whether the future common neighbors contribute to the current link prediction, we propose the similarity-based future common neighbors (SFCN) model for link prediction, which accurately locate all the future common neighbors besides the current common neighbors in networks and effectively measure their contributions. We also design and observe three MATLAB simulation experiments. The first experiment, which adjusts two parameter weights in the SFCN model, reveals that the future common neighbors make more contributions than the current common neighbors in complex networks. And two more experiments, which compares the SFCN model with eight algorithms in five networks, demonstrate that the SFCN model has higher accuracy and better performance robustness.
链路预测旨在通过网络信息预测未知链路的存在。然而，大多数基于相似度的算法仅利用当前的共同邻居信息，在演化网络中无法获得足够高的预测准确率。因此，本文首先定义了未来共同邻居，即在未来能够转变为共同邻居的节点。为了分析未来共同邻居是否有助于当前的链路预测，我们提出了基于相似度的未来共同邻居（SFCN）链路预测模型，该模型能够准确地定位网络中除当前共同邻居之外的所有未来共同邻居，并有效地衡量它们的贡献。我们还设计并观察了三个MATLAB仿真实验。第一个实验调整了SFCN模型中的两个参数权重，结果表明在复杂网络中未来共同邻居比当前共同邻居的贡献更大。另外两个实验在五个网络中将SFCN模型与八种算法进行了比较，结果表明SFCN模型具有更高的准确率和更好的性能鲁棒性。
