基于资源分配与图嵌入加权的链路预测算法

融入权重信息的加权链路预测算法大都具有更好的预测效果，现有的大多数加权算法都是基于外部权重信息，基于网络拓扑结构权重的研究较少。针对此问题，提出一种利用无权网络的结构特征生成结构权重的加权链路预测算法。首先计算资源分配指标得到网络局部结构相似性，再利用DeepWalk算法学习网络结构特征生成节点向量得到余弦相似性，将2个相似性结合定义出网络的结构权重。最后在4个数据集上进行实验，将融入权重信息的3种不同类型相似性指标W-CN、W-LP、W-RWR与对应的无权指标进行对比。结果表明，融入结构权重信息的预测算法具有更高的预测精度。

Link Prediction Algorithm Based on Resource Allocation and Graph Embedding Weighting

Most of the weighted link prediction algorithms with weight information have higher accuracy. However, most of the existing weighting algorithms are based on the external weight information, and there are few studies based on the weight of network topology. To solve this problem, a weighted link prediction algorithm is proposed, which uses the structural features of the unweighted network to generate the structure weight. Firstly, the resource allocation index is calculated to obtain the local structure similarity of the network. Then, the DeepWalk algorithm is used to learn the network structure features to generate the node vector to obtain the cosine similarity. The two similarities are combined to define the network structure weight. Finally, experiments are carried out on four network datasets, and three different types of similarity indices W-CN, W-LP and W-RWR are compared with those without weight information. The results show that the algorithm with structural weight information has higher prediction accuracy.