融合节点中心性和度相关聚类的有向网络链路预测  

Link Prediction in Directed Network Combining Node Centrality and Degree-related Clustering

作　　者：陈广福 连雁平 CHEN Guang-fu;LIAN Yan-ping(College of Mathematics and Computer Science,Wuyi University,Wuyishan 354399,China;Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry,Wuyishan 354399,China)

机构地区：[1]武夷学院数学与计算机学院,福建武夷山354399 [2]福建省茶产业大数据应用与智能化重点实验室,福建武夷山354399

出　　处：《计算机与现代化》2022年第8期36-42,共7页Computer and Modernization

基　　金：福建省自然科学基金资助项目(2021J011146);武夷学院引进人才科研启动基金资助项目(YJ202017)。

摘　　要：现存大部分有向网络的链路预测方法仅关注链接方向信息和互惠链接信息而忽略节点重要性及度相关聚类的贡献,导致预测精度下降。针对以上不足,提出基于节点中心性和度相关聚类的有向网络链路预测指标。首先,利用节点中心性统计任意节点邻居数量去衡量节点的影响力;其次,将节点度相关聚类系数方法扩展到有向网络去评估节点聚类能力,并与网络同配系数相融合获得节点对高聚类能力;最后,融合以上2类信息提出一个带参的有向网络链路预测指标。在6个真实世界有向网络上与最近代表性预测指标比较,所提指标AUPR和AUC分别提高了33%和1.6%。Most existing link prediction methods of directed networks only focus on link direction information and reciprocal link information,but ignore the contribution of node importance and degree correlation clustering,which leads to the decrease of prediction accuracy.To solve these problems,a directed network link prediction algorithm based on node centrality and degree correlation clustering is proposed.Firstly,it uses node centrality to calculate the number of neighbors of any node to measure the influence of node.Secondly,the node degree correlation clustering coefficient method is extended to the directed network to evaluate the clustering ability of nodes,and it is combined with the network coordination coefficient to obtain the high clustering ability of nodes.Finally,a parametric directed network link prediction index is proposed by integrating the above two kinds of information.On the six real world network compared with recent representative methods,the AUPR and AUC of the proposed algorithm are improved 33%and 1.6%respectively.

关 键 词：有向网络 链路预测 节点中心性 度相关聚类系数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]