基于拓扑连接紧密度的相似性链路预测算法  

Link prediction method for complex network based on closeness between nodes

作　　者：丁大钊[1] 陈云杰[1] 靳彦青[1] 刘树新[1] 

机构地区：[1]国家数字交换系统工程技术研究中心,郑州450002

出　　处：《计算机应用》2017年第8期2129-2132,2138,共5页journal of Computer Applications

基　　金：国家863计划项目(2015AA01A708;2016YFB0801605)~~

摘　　要：许多链路预测方法仅仅关注预测的准确度衡量指标,忽略了精确度衡量标准在实际应用中的重要作用,且没有考虑共同邻居与预测节点间紧密度对相似性刻画的影响。针对上述问题,提出了一种基于拓扑连接紧密度的相似性链路预测算法。该方法通过局部拓扑结构定义共同邻居紧密度,并引入参数调节不同网络中紧密程度,最终刻画网络节点间的相似度。6个实际网络测试表明,相比共同邻居(CN)、资源分配(RA)、Adamic-Adar(AA)、局部路径(LP)、Katz等相似性指标,该算法提升了链路预测的预测精度。Many link prediction methods only focus on the standard metric AUC（ Area Under receiver operating characteristic Curve）, ignoring the metric precision and closeness of common neighbors and endpoints under different topological structures. To solve these problems, a link prediction method based on closeness between nodes was proposed. In order to describe the similarity between endpoints more accurately, the closeness of common neighbors was designed by considering the local topological information around common neighbors, which was adjusted for different networks through a parameter. Empirical study on six real networks show that compared with the similarity indicators such as Common Neighbor（ CN）, Resource Allocation（ RA）, Adamic-Adar（ AA）, Local Path（ LP） and Katz, the proposed index can improve the prediction accuracy.

关 键 词：复杂网络 链路预测 紧密度 相似性 拓扑结构 

分 类 号：N393.02[自然科学总论]