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

arXiv: https://arxiv.org/abs/1610.09769 · HTML: https://mlsanity.com/paper/1610.09769/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2016.

## Built on by (most cited first, 11 of 11)

- [RelGNN: Composite Message Passing for Relational Deep Learning](https://mlsanity.com/paper/2502.06784/) (2025)
- [Learning the Implicit Semantic Representation on Graph-Structured Data](https://mlsanity.com/paper/2101.06471/) (2021)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)](https://mlsanity.com/paper/2202.09021/) (2022)
- [Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning](https://mlsanity.com/paper/2108.13886/) (2021)
- [SHGNN: Structure-Aware Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2112.06244/) (2021)
- [GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs](https://mlsanity.com/paper/2208.06144/) (2022)
- [Meta-Path-Free Representation Learning on Heterogeneous Networks](https://mlsanity.com/paper/2102.08120/) (2021)
- [HeMI: Multi-view Embedding in Heterogeneous Graphs](https://mlsanity.com/paper/2109.07008/) (2021)
- [An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers](https://mlsanity.com/paper/2203.16751/) (2022)
- [Data-Driven Self-Supervised Graph Representation Learning](https://mlsanity.com/paper/2412.18316/) (2024)

## Abstract (arXiv)

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.