# Heterogeneous Information Network Embedding for Meta Path based Proximity

arXiv: https://arxiv.org/abs/1701.05291 · HTML: https://mlsanity.com/paper/1701.05291/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2017.

## Built on by (most cited first, 5 of 5)

- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Relational Graph Convolutional Networks: A Closer Look](https://mlsanity.com/paper/2107.10015/) (2021)
- [Zoo Guide to Network Embedding](https://mlsanity.com/paper/2305.03474/) (2023)
- [A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks](https://mlsanity.com/paper/2108.03953/) (2021)
- [Sparsity-aware neural user behavior modeling in online interaction platforms](https://mlsanity.com/paper/2202.13491/) (2022)

## Abstract (arXiv)

A network embedding is a representation of a large graph in a low-dimensional space, where vertices are modeled as vectors. The objective of a good embedding is to preserve the proximity between vertices in the original graph. This way, typical search and mining methods can be applied in the embedded space with the help of off-the-shelf multidimensional indexing approaches. Existing network embedding techniques focus on homogeneous networks, where all vertices are considered to belong to a single class.