  Graph is an important data representation which occurs naturally in the real
world applications \cite{goyal2018graph}. Therefore, analyzing graphs provides
users with better insights in different areas such as anomaly detection
\cite{ma2021comprehensive}, decision making \cite{fan2023graph}, clustering
\cite{tsitsulin2023graph}, classification \cite{wang2021mixup} 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 \textit{subgraph2vec} 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.
