Social network analysis (SNA) is one of the important applications of graph theory. A network graph analysis is considered social network analysis if the following apply:

• The vertices of the graph represent people.
• The edges between them represent social relationships between them, such as a friendship, a common hobby, kinship, a sexual relationship, dislikes, and so on. 
• The business question that we are trying to answer through graph analysis has some strong social aspect to it.

Human behavior is reflected in SNA and should always be kept in mind while working on SNA. By mapping human relationships in a graph, SNA gives good insights into human interactions, which can help us understand their actions.

By creating a neighborhood around each individual and analyzing the actions of an individual based on its social relationship, you can produce interesting, and sometimes surprising, insights. The alternative approaches to analyzing individuals in isolation, based on their individual job functions, can only provide limited insights.

So, SNA can be used for the following:

• Understanding a users's actions on social media platforms, such as Facebook, Twitter, or LinkedIn
• Understanding fraud
• Understanding society's criminal behavior

Thus, SNA—due to its inherent distributed and interconnected architecture of social networks—is one of the most powerful use cases for graph theory. Another way to abstract a graph is by considering it as a network and applying an algorithm designed for networks. This whole area is called network analysis theory, which we will discuss next.