基于综合游走策略的边嵌入链路预测算法

针对网络分析方法中研究的图数据默认使用节点图，只能得到节点的向量表示，不能直接将边表示成向量的问题，设计了一种基于有偏+无偏的图嵌入算法Line2Vec，并在此基础上提出基于边嵌入的链路预测框架(Line2Vec-L).首先，基于综合游走策略重新定义采样域节点的采样概率，并结合Word2Vec模型得到信息未被稀释、表示性强的节点图的边嵌入向量；然后，结合关联矩阵得到不存在边或未知边的向量表示，并将得到的边向量用于链路预测.实验结果表明Line2Vec在边向量表示上的有效性，并验证了Line2Vec-L的AUC值更高，由此说明采用Line2Vec可得到表示性更强的边向量，有助于提升链路预测的性能.

Edge-embedding link prediction algorithm based on comprehensive walk strategy

As a default graph dataset, the node graph is often used in network analysis. By employing this method, the vector representation of nodes can be obtained, but the edges cannot be directly represented as vectors. In this paper, a graph embedding algorithm named Line2Vec is proposed to combine biased sampling with unbiased sampling. And on this basis, a link prediction framework named Line2Vec-L is suggested. Firstly, Line2Vec redefines the sampling probability of nodes in sampling domain based on the above comprehensive walk strategy. And by combining Word2Vec model, the enriching edge-embedding vector representation of node graph is obtained to avoid impairing the information. Then. the vector representation for non-existing edges and unknown edges can be learned with incidence matrix. All the obtained vectors of edges are applied to link prediction. Experiments show that Line2Vec is effective on edge-vector representation and the performance of AUC with Line2Vec-L is better. In other words, it means the quality of edge-vector representation generated by Line2Vec is higher, which helps to improve the performance of link prediction.