嵌入
计算机科学
相似性(几何)
可扩展性
理论计算机科学
顶点(图论)
最近邻搜索
数据挖掘
情报检索
人工智能
图像(数学)
数据库
图形
作者
Jingbo Shang,Meng Qu,Jialu Liu,Lance Kaplan,Jiawei Han,Jian Peng
出处
期刊:Cornell University - arXiv
日期:2016-01-01
被引量:113
标识
DOI:10.48550/arxiv.1610.09769
摘要
Most real-world data can be modeled as heterogeneous information networks (HINs) consisting of vertices of multiple types and their relationships. Search for similar vertices of the same type in large HINs, such as bibliographic networks and business-review networks, is a fundamental problem with broad applications. Although similarity search in HINs has been studied previously, most existing approaches neither explore rich semantic information embedded in the network structures nor take user's preference as a guidance. In this paper, we re-examine similarity search in HINs and propose a novel embedding-based framework. It models vertices as low-dimensional vectors to explore network structure-embedded similarity. To accommodate user preferences at defining similarity semantics, our proposed framework, ESim, accepts user-defined meta-paths as guidance to learn vertex vectors in a user-preferred embedding space. Moreover, an efficient and parallel sampling-based optimization algorithm has been developed to learn embeddings in large-scale HINs. Extensive experiments on real-world large-scale HINs demonstrate a significant improvement on the effectiveness of ESim over several state-of-the-art algorithms as well as its scalability.
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