符号网络中融合聚集系数与符号影响力的链路预测算法

Link prediction algorithm in signed networks based on clustering coefficient and sign influence

为快速、准确地实现符号社会网络中的链接预测与符号预测双重目标,提出一种融合共同邻居节点的聚集系数与连边符号影响力的链路预测算法.基于结构平衡理论,有效利用节点的度、聚集系数、路径上的中间传输节点、连边符号及其影响力等信息,分别定义了两节点基于一阶共同邻居和二阶共同邻居的相似性,最终得到两节点的总相似性得分,用其绝对值度量两节点建立链接的可能性,通过其符号获得链接的符号预测结果,从而实现符号网络中的链路预测.在6个有代表性的符号网络数据集上进行了实验,以AUC、调整的Precision’、Accuracy等为评价指标,对比了多个符号网络链接预测算法,并进行了可调步长参数的敏感性分析.实验结果表明,所提算法在符号网络链接预测与符号预测两方面均达到了较好的性能,无论是稀疏网络还是负链接预测,准确性均高于其他算法.

In order to achieve the dual goals of link prediction and sign prediction in signed social networks quickly and accurately,a link prediction algorithm is proposed based on the clustering coefficient of common neighbor nodes and the influence of the sign of edges.With the structural balance theory,the similarity of the two nodes based on their first-order common neighbors and the second-order common neighbors is defined respectively by using the degree,clustering coefficient,intermediate transitive nodes,and the influence of the sign of the edge,the total similarity score of the two nodes is finally obtained and its absolute value is used to measure the possibility to establish a link of the two nodes,then its sign is the sign prediction result of the link.Accordingly,the link prediction and sign prediction are realized in signed networks.Experiments have been carried out on six representative signed network datasets,with evaluation indicators such as AUC,adjusted precision'and accuracy.The experiment results are compared with several link prediction algorithms in signed networks the sensitivity of adjustable step size parameters is also analyzed.Experimental results show that the proposed algorithm can achieve good performance in both link prediction and sign prediction,and its accuracy is higher than other algorithms for both sparse networks and the prediction of negative links.