The importance of machine learning for social network analysis is realized as an inevitable tool in forthcoming years. This is due to the unprecedented growth of social-related data, boosted by the proliferation of social media websites and the embedded heterogeneity and complexity. Alongside the machine learning derives much effort from psychologists to build computational model for solving tasks like recognition, prediction, planning and analysis even in uncertain situations. Therefore, it is significant to study the synergy of machine learning techniques in social network analysis, focus on practical applications, and open avenues for further research. In this paper, we have reviewed the theoretical aspects of social network analysis with a combination of machine learning-based techniques, its representation, tools and techniques used for analysis. Additionally, the source of data and its applications are also highlighted in this paper.

The Social Network (2012) is defined as “a social formation comprising a subset of actors and the interaction between these actors.” This theoretical formation is useful to analyze interrelation between individuals, groups, organizations, or even the societies. Researchers (Social Network Analysis, 2012) also studied how these social formation influences other elements, how it changes relative density of ties, or how formation changes over time (network dynamics).

In recent years, social network approach has become increasingly relevant in computer information revolution (“The number of published applications has been growing at about 250% per year over the past five years”—Wasserman and Faust, 1994a). With the proliferation of web technologies, there is an increasingly greater amount of interaction by people while on the Internet. Web has enabled many ways of interaction (White and Harary, 2001), which forms the social network structures (Wellman, 2001). This phenomenon was fueled by the advent of Web 2.0 (Hendler and Goldbeck, 2008). Social Networks have ushered in a multidimensional approach to handle problems in social domains. It provides new angles of thought towards old problems (Bearman et al., 1999). The Internet imposes new questions on the nature of social networks and opens new perspectives for Social Network Analysis (SNA) (Nadel, 1957; Wasserman and Faust, 1994a; and http://www.semioticon.com/ semiotix/ semiotix14/sem-14-05.html). SNA is sophisticated academic field which combines sociology, social psychology along with graph theory and statistics. A social network operates on many levels, from personal friendship to across nations. It has a vital impact on the way problems are being solved, corporations are being run, and individuals are being motivated towards personal objectives.

Information Technology Journal, Social Networks, Machine learning, Clustering.