异质网络中基于节点影响力的相似度度量方法  

Node Influence Based Similarity Measure Method in Heterogeneous Network

作　　者：刘露 胡封晔[4] 牛亮[5] 彭涛[1,2,3] LIU Lu;HU Feng-ye;NIU Liang;PENG Tao(College of Software,Jilin University,Changchun,Jilin 130012,China;Key Laboratory of Symbolic Computation and Knowledge Engineering(Jilin University),Ministry of Education,Changchun,Jilin 130012,China;College of Computer Science and Technology,Jilin University,Changchun,Jilin 130012,China;College of Communication Engineering,Jilin University,Changchun,Jilin 130012,China;The First Hospital of Jilin University,Changchun,Jilin 130012,China)

机构地区：[1]吉林大学软件学院,吉林长春130012 [2]符号计算与知识工程教育部重点实验室(吉林大学),吉林长春130012 [3]吉林大学计算机科学与技术学院,吉林长春130012 [4]吉林大学通信工程学院,吉林长春130012 [5]吉林大学第一医院,吉林长春130012

出　　处：《电子学报》2019年第9期1929-1936,共8页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61872163,No.61806084);中国博士后科研基金项目(No.2018M631872);吉林省教育厅项目(No.JJKH20190160KJ);吉林省科技厅重点科技研发项目(No.20180201044GX)

摘　　要：异质网络相似度学习,即分析两个不同类型对象间的相关程度.不同类型对象在异质网络中的重要程度不同,它们在相似度学习过程中的发挥的作用也不同.针对异质网络,提出了一种基于节点影响力的相似度度量方法NISim,该模型既考虑了网络中的链接结构,也保留了网络中的语义信息,同时区分不同类型节点对异质网络的作用.在异质信息网络环境下,通过启发式规则区分并量化不同类型节点的影响力权值,并结合网络链接结构和节点间语义关系,解决了提高相似度学习准确性的问题.实验结果表明,该方法能够有效地对异质信息网络不同类型节点进行相似度度量,可以应用在网络搜索、推荐系统以及知识图谱构建等不同领域.Heterogeneous network similarity learning is to analyze the degree of correlation between two different types of objects.Different types of objects have different degrees of importance in heterogeneous networks,and play different roles in the similarity learning process.This paper proposes a node influence based similarity measure method(NISim) heterogeneous information network.This method not only considers the link structure in network but also keeps the semantic information in heterogeneous networks.Also,this method distinguishes the effect to heterogeneous network brought by different types of nodes.In heterogeneous network,the heuristic rules are used to distinguish and quantify the influence weight of different types of nodes.In addition,the link structure in network and the semantic relationship are combined to solve the problem of improving similarity learning accuracy.Experimental results show that this method can measure the similarity between different types of nodes effectively.It can be applied in different fields such as network search,recommendation system and knowledge graph construction and so on.

关 键 词：数据挖掘 异质网络 推荐系统 知识图谱 网络搜索 节点影响力 链接结构 语义关系 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]