<p>Machine learning, deep learning and NLP methods on graphs are vastly present in different fields and have important roles in various domains from self-driving cars to friend recommendations on social media platforms. However, to apply these methods on graphs, the data usually need to be in an acceptable size and format. In fact, graphs normally have high dimensions, and therefore we need to transform them to a low-dimensional vector space. Embedding is a low-dimensional space into which one can translate high-dimensional vectors in a way that intrinsic features of the input data are preserved. In this review, we first explain the importance of graphs and the embedding methods applied to them. Next, we will review some of the random walk-based embedding methods as well as their strengths and weaknesses that have been developed recently. Later, we will address research directions for future research.</p>