融合节点标签与强弱关系的链路预测算法

Link Prediction Algorithm Fusing Node Label and Strength Relationship

如何通过已知路径结合相关属性信息和不同关系强度进行链路预测是一个难题。为了解决这个问题,提出融合节点标签与强弱关系的链路预测算法。选取两个中心节点,采用基于双半径节点标签算法计算以其为中心的所有网络节点标签;生成中心节点带有节点标签的h深度局部子图;提取局部子图并将其作为目标网络获得特征矩阵,在对特征矩阵进行矩阵分解的同时融入节点属性信息与强弱关系,赋予动态权值,构建相似度矩阵。实验结果表明,与常见的基于共同邻居算法、基于网络嵌入等链路预测算法相比,该算法的精确度最高提升1.83%,且其预测结果的精确度和效率明显提升,同时能够有效且准确地挖掘各节点的内部相关性。

It is a difficult problem how to make link prediction through known paths combined with relevant attribute information and different relationship strengths. To solve this problem, a link prediction algorithm that fuses node labels and strong and weak relationships is proposed. Two central nodes are selected, and all network node labels centered on them are calculated by the algorithm based on the double radius node label. The h-depth local sub-graph with the node label of the central node is generated. the local sub-graph is extracted and used as the target network. The feature matrix is obtained,and then the feature matrix is matrix decomposing into the node attribute information and the strong and weak relationship, and dynamic weights are assigned to construct the similarity matrix. The experimental results show that compared with the common link prediction algorithms based on common neighbor algorithm and network embedding, the accuracy of this algorithm is improved by up to 1.83%, and the accuracy and efficiency of its prediction results are significantly improved. At the same time, it can be effectively and accurately mined the internal correlation of each node.