Abstract
Social networks are usually represented as (static) graphs; their vertices correspond to individuals and edges represent their mutual relationships. With the aim to study effectively also their dynamic versions, we will discuss here various tools developed for social network analysis based on their performance on big graph data evolving in time, support for parallel computations, algorithms they provide and their flexibility for the user.