异质网络中基于节点影响力的相似度度量方法

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.

Node Influence Based Similarity Measure Method in Heterogeneous Network

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.