一种改进的加权网络链接预测方法

摘要

Currently,link mining in complex networks has been extensively studied.However,there are only a few related works on weighted networks and the results are not satisfactory.A new link prediction method for weighted networks was proposed by improving the weighted similarity measure of network structure.The new method is based on the assumption that when the link xz is strong and the link zy is weak,the link has the least contribution to the link between node x and y.Therefore,in the new method,as the link xz is strong and the link zy is weak,the degree of weakening of the link to the contribution degree of the similarity score S(x,y) between the node x and y is maximal.Comparative experiments on weighted dataset USAir and NetScience show that the proposed method has better performance in AUC indicators.%目前,复杂网络的链接挖掘问题已得到了广泛研究,而加权网络的相关研究还较少且结果不甚理想.鉴于此,提出一种新的针对加权网络的链接预测方法,对以往方法中的加权相似性度量进行改造.新方法主要基于这一假定:链接xz为强关系而链接zy为弱关系时,链路对节点x和y之间形成链接的贡献最低.因此,新方法中链接xz为强关系而链接zy为弱关系时,链路对节点x和节点y之间的相似性得分S(x,y)的贡献度的削弱程度最大.在带权网络数据集USAir和NetScience上的比较实验表明,新方法在AUC指标上具有一定的优势.