High dimensionality of data is a challenging scenario in the current era as the digital transformation of the society is in process. This problem is particularly complex in social networks as in such systems, it is coupled with other challenges such as interdependency of data points and heterogeneity of data sources. To overcome such disadvantages and aid in creation of downstream applications for social network analysis, network embedding techniques have been proposed. These techniques, in themselves, are not important but are the backbone of various network-based applications. Due to the scientific interest in this domain there has been a mushrooming of embedding techniques. It has therefore become crucial to learn the intuitions behind these techniques in order to compare and contrast them. The current analytical study is drawn with the following broad objectives: providing practitioners with understanding of network representative learning mathematical study of state-of-the-art techniques and highlighting the evolution of the literature in this field.