Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

Abstract

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.

References in corpus (1)

• Network Lasso: Clustering and Optimization in Large Graphs

Cited by in corpus (13)

• HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding
• Relation Structure-Aware Heterogeneous Information Network Embedding
• Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
• Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
• Learning the Implicit Semantic Representation on Graph-Structured Data
• HeMI: Multi-view Embedding in Heterogeneous Graphs
• Neural Embedding Propagation on Heterogeneous Networks
• Meta-Path-Free Representation Learning on Heterogeneous Networks
• Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
• Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery
• Predicting Drug Responses by Propagating Interactions through Text-Enhanced Drug-Gene Networks
• GAHNE: Graph-Aggregated Heterogeneous Network Embedding
• Multi-View Dynamic Heterogeneous Information Network Embedding