Social interactions within networks comprise an increasing event nowadays. Different aspects of societies and competitive markets, such as Social Medias through the Internet and Telecommunications environments hold a high correlation with the social network analysis methodology. By understanding these social structures and its interactions might be possible to realize how individuals and consumers relate each other and hence predict further social structures in the future. However, most of the current social network analysis projects are in relation to static structures, not considering how the social network evolves over the time. The dynamic approach can points out new perspectives in terms of social network analysis, including prediction and simulations scenarios. In order to perceive the social network relations over the time is crucial to collect the distinct snapshots of the social structure, understanding not just how the social members relate each other but in addition to that how this relationships evolves over the time. Measures in relation to social network describe nodes and links by static metrics, depicting its strength, its overall distances to the other related nodes, and its amounts of connections, among others. This dynamic approach makes possible to create a historical data, quite usual for predictive modeling. As such, new social network measures and algorithms should be created in order to describe dynamic features assigned to social structures over the time.