异构信息网络的相似性度量方法

摘要

随着社交网络和文献索引网络等大规模互联多类异质信息网络的浮现,为相似搜索提出许多挑战,其中相似性度量是关键问题之一.现有适用于同构网络的相似度量方法没有考虑网络多个路径的不同语义.本文提出一种新的基于元路径的相似性度量方法,可以在异构网络中搜索相同类型的对象.元路径是由在不同对象类型中定义的一系列关系所组成的路径,可以为网络中相似搜索引擎提供共同的基础.在真实数据集上的实验表明,与无序相似性衡量方法相比,本文提出的方法支持快速路径相似性查询,可广泛地应用于社交网络和电子商务领域.%The emerging of large-scale interconnection networks such as social networks and bibliographic networks raised many challenges for similarity search, wherein the similarity measure is one of the key issues.The existing methods of similarity meas-ure did not consider the different semantic of multiple paths on homogeneous networks.This paper proposes a novel similarity measure method based on meta path, which can search the same type of objects on heterogeneous networks.Meta path is path consisting of a series of relationships defined in the different object types, which provides a common basis for the similarity search engines on the networks.Experiments on real data sets show that compared to the disorder similarity measures, the proposed method supports fast path similarity query, and it can be widely used in social networks and e-commerce.