融合节点重要性的无监督链路预测算法

Unsupervised Link Prediction Algorithm Fusing Node Importance

提高加权网络链路预测算法精确度是研究复杂网络的基础问题之一。常用的基于局部网络结构的无监督预测方法没有考虑到重要性越高的节点越容易产生新连接,且在真实网络上中心性小的节点同样具有高度重要性。针对上述问题,提出一种融合节点重要性的无监督链路预测算法,从结构相似性和节点重要性两个角度计算新连接产生的可能性,并利用自定义系数调节影响程度。在5个真实加权网络数据集上进行实验,结果表明在解决小规模加权网络的快速预测问题上,该算法相比同类方法的预测精确度更高,有监督式链路预测方法并不适用。

Improving the accuracy of weighted network link prediction algorithm is one of the basic problems in the study of complex networks.The common unsupervised prediction methods based on local network structure do not take into account that the nodes are more important,the they are more likely to generate new connections,and nodes with lower centrality are also highly important in real networks.To solve these problems,an unsupervised link prediction algorithm fusing node importance is proposed.In this algorithm,the possibility of new connections is calculated from the perspec-tives of structural similarity and node importance,and the influence degree is adjusted by using the custom coefficient.Experimental results on five real weighted network datasets show that,in solving the problem of fast prediction of small-scale weighted networks,the algorithm is more accurate than the same type of methods,and the supervised link prediction method is not applicable.