  Dimensionality reduction techniques map data represented on higher dimensions
onto lower dimensions with varying degrees of information loss. Graph
dimensionality reduction techniques adopt the same principle of providing
latent representations of the graph structure with minor adaptations to the
output representations along with the input data. There exist several cutting
edge techniques that are efficient at generating embeddings from graph data and
projecting them onto low dimensional latent spaces. Due to variations in the
operational philosophy, the benefits of a particular graph dimensionality
reduction technique might not prove advantageous to every scenario or rather
every dataset. As a result, some techniques are efficient at representing the
relationship between nodes at lower dimensions, while others are good at
encapsulating the entire graph structure on low dimensional space. We present
this survey to outline the benefits as well as problems associated with the
existing graph dimensionality reduction techniques. We also attempted to
connect the dots regarding the potential improvements to some of the
techniques. This survey could be helpful for upcoming researchers interested in
exploring the usage of graph representation learning to effectively produce
low-dimensional graph embeddings with varying degrees of granularity.
