The basic assumption in data mining is that observations are i.i.d. (independent and identically distributed), which is often not the case when observations are not independent but rather are interconnected, such as in interacting individuals. Social network analysis (SNA) brings ways to handle this nonindependence by exploiting the structure of connections between observations.