稀疏网络的传统链接预测准确率低, 为了捕捉稀疏网络节点间建立链接的可能性, 提出基于节点间最短路径的最简子图概念.最简子图反映了节点间的拓扑紧密关系, 在采用node2vec节点向量化方法的基础之上, 实现了基于最短路径的链接表示, 并采取长短期记忆循环神经网络 (LSTM) 学习长链接节点序列的特征, 最终实现链接的分类.实验结果表明, 该方法与已有方法相比, 在4种不同数据集上的预测AUC值平均提高了11.6％, AP值平均提高了13.3％.%Traditional link prediction methods of sparse networks have low accuracy. In order to capture the possibility of link establishment among sparse network nodes, we propose the concept of simplest subgraph based on the shortest path between a pair of nodes, which reflects the tight topology relationship between nodes. Based on the node2 vec node vectorization method, the link representation based on the shortest path is implemented. To complete the link classification task, we use the long short-term memory (LSTM) recurrent neural network to learn the characteristics of long-link node sequences. Compared with existing methods, the proposed method can increase the AUC value on 4 different datasets by 11.6％ averagely, and the AP value by an average of 13.3％.
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