基于拓扑有效连通路径的有向网络链路预测方法  

A Method of Link Prediction in Directed Network Based on Effective Connectivity Path

作　　者：李治成 吉立新 刘树新 李星 李劲松 LI Zhi-cheng;JI Li-xin;LIU Shu-xin;LI Xing;LI Jin-son(People’s Liberation Army Strategic Support Force Information Engineering University,Zhengzhou 450001)

机构地区：[1]中国人民解放军战略支援部队信息工程大学,郑州450001

出　　处：《电子科技大学学报》2021年第1期127-137,共11页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(61803384)。

摘　　要：链路预测旨在利用已有的网络拓扑信息来挖掘未知连边,具有较高的应用价值。大部分已有的基于拓扑结构的方法,关注节点对之间的路径数或者预测节点对的出入度,未有效挖掘节点对之间的连边长度和连边上节点的影响力对相似性的影响。针对此问题,该文提出了基于拓扑有效连通路径的链路预测方法,并分析了不同路径长度在节点度、半局部中心性和H-指数这3种不同衡量节点影响力指标下对节点相似性的贡献。通过8个真实网络仿真,发现H-指数能有效量化节点的局部影响力,且在3种衡量指标下均具有较高的预测精度。Link prediction aims to mine unknown links based on observed topology information,which has high application value in many fields.At present,existing link prediction methods mainly focus on undirected network while the research of directed network is less.The prediction method based on structural information assumes that the more similar the nodes are,the more likely they are to be linked.Actually,the links between nodes are generated through paths,which cause similarity transfers between nodes.Most of the existing methods based on topology often focus on either the path between node pairs or the node degree,do not effectively mine the link length between node pairs and the local influence of nodes on the path.To solve this problem,this paper proposes a link prediction algorithm based on the effective connectivity path,and analyzes the contribution of different path length and node degree,semi-local centrality and H-index to node similarity.Compared with the existing eight prediction methods,the method proposed based on H-index effectively quantifies the local influence of nodes,and has a higher prediction accuracy under three indices.

关 键 词：有向网络 有效连通路径 链路预测 节点影响力 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]