融合多元影响力节点识别指标MPR的链接预测

多元网络通常是指节点之间存在多种维度链接关系的图结构.多元网络链接预测算法在构建相似度指标时,多数仅考虑单一维度网络的拓扑结构属性,未挖掘不同维度子网络之间存在的关联,影响链接预测的效果.针对该问题,提出一种基于多元全局节点影响力识别指标MPR的多元网络链接预测算法.通过定义一个多维度节点影响力排序指标MPR,度量多元网络空间中影响力较大的节点,并把影响力排名函数转化为潜在节点对之间的相似度得分,从而应用到多元网络链接预测场景中.在2个真实多元网络数据集上的实验结果表明,该算法的预测效果优于PR、EDC、ANC等对比算法,且具有较好的稳定性.

Link Prediction Based on Multiplex Influential Node Identification Index MPR

Multiplex network is a graph structure in which multiple link relations exist between nodes.When constructing similarity index,most of existing multiplex network link prediction algorithms consider only the topological attributes of a single-dimensional network,and fails to mine the relations between sub-networks of different dimensions,which undermines the performance of link prediction.To address the problem,this paper proposes a multiplex network link prediction algorithm based on multiplex global node influence identification index,Multiplex PageRank(MPR).By defining a multiplex node influence ranking index MPR,the nodes with greater influence in the multiplex network space can be measured.Then,the influence ranking function is converted into the score of similarity between two nodes in each potential node pair,and applied to the multiplex network link prediction scene.Experimental results on two real multiplex network datasets show that the proposed algorithm outperforms PR,EDC,ANC and other algorithms,and has better stability.