In the age of Facebook and Twitter, social networks are a mainstay of the human experience that, in the simplest of terms consist of a set of actors-individuals, communities, or even organizations-and the connections among them. “Connections” can represent any number of relationships between pairs of actors: familial ties, friendships, acquaintances, frequent interactions, exchange partnerships, or political alliances, among others. Based in graph theory, social network analysis (SNA) is a developing fi eld that most often evaluates these kinds of connections in today’s world, as a means of systematically exploring interaction. The graph theory has been widely used in SNA due to its representational capacity and simplicity. Essentially, the graph consists of nodes and of connections which connect the nodes. In social networks the representation by graphs is also called sociogram, where the nodes are the actors or events and the lines of connection establish the set of relationships in a two dimensional drawing. Graphs enable many interesting analyses to be made and have visual appeal which helps us to understand the network under study. However, for networks with many actors and connections, this becomes impossible. Similarly, some important information, such as the frequency of occurrence and of specifi c values, are diffi cult to apply in a graph. To resolve this problem, we use the matrices developed by sociometrics which produce what we call sociomatrices. Thus, sociometrics and its sociomatrices complement graph theory, establishing a mathematical basis for the analysis of social networks.