Social networks such as Twitter or Facebook are part of the phenomenon called
, a term used to describe very large and complex data sets. To represent these networks, the connections between users can be easily represented using (directed) graphs. In this paper, we are mainly focused on two different aspects of social network analysis. First, our goal is to find an efficient and high-level way to store and process a social network graph, using reasonable computing resources (processor and memory).We believe that this is an important research interest, since it provides a more democratic method to deal with large graphs.Next, we turn our attention to the study of
, a specific kind of users on Twitter. Roughly speaking, such users try to gain visibility by following other users regardless of their contents. Using two similarity measures called
, we show that such users may be detected and classified very efficiently.