# Subgraph2vec: A random walk-based algorithm for embedding knowledge graphs

arXiv: https://arxiv.org/abs/2405.02240 · HTML: https://mlsanity.com/paper/2405.02240/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 12 of 12)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning](https://mlsanity.com/paper/2010.16103/) (2020)
- [Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation](https://mlsanity.com/paper/2112.10063/) (2021)
- [Differentiating Concepts and Instances for Knowledge Graph Embedding](https://mlsanity.com/paper/1811.04588/) (2018)
- [Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights](https://mlsanity.com/paper/1910.00004/) (2019)
- [Steps to Knowledge Graphs Quality Assessment](https://mlsanity.com/paper/2208.07779/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Survey on Recent Random Walk-based Methods for Embedding Knowledge Graphs](https://mlsanity.com/paper/2406.07402/) (2024)

## Abstract (arXiv)

Graph is an important data representation which occurs naturally in the real world applications citegoyal2018graph. Therefore, analyzing graphs provides users with better insights in different areas such as anomaly detection citema2021comprehensive, decision making citefan2023graph, clustering citetsitsulin2023graph, classification citewang2021mixup and etc. However, most of these methods require high levels of computational time and space. We can use other ways like embedding to reduce these costs. Knowledge graph (KG) embedding is a technique that aims to achieve the vector representation of a KG. It represents entities and relations of a KG in a low-dimensional space while maintaining the semantic meanings of them. There are different methods for embedding graphs including random walk-based methods such as node2vec, metapath2vec and regpattern2vec. However, most of these methods bias the walks based on a rigid pattern usually hard-coded in the algorithm. In this work, we introduce textitsubgraph2vec for embedding KGs where walks are run inside a user-defined subgraph. We use this embedding for link prediction and prove our method has better performance in most cases in comparison with the previous ones.