The complexity of the information that may be extracted from observations of a source, as a phenomenological observation of its behavior, without any prior knowledge of the source’s internal structure, may reveal missed regularities and structural properties hidden behind the apparent randomness of the stochastic process that the source generates. This information can provide useful clues in order to predict the source future behavior or its characteristic properties. In social network dynamics each actor generally performs some sort of action, communication acts; exchange of capital or economic goods or any other possible social interplay or attribute for which a well defined time limit and intensity can be pinpointed in time. This definition can generally be extended and even applied to objects as for example a correlation of goods purchased by each client of a supermarket. The agent that determines the dynamic nature of a network is primarily Time. Topologically a network that evolves as the one depicted in figure 1 is completely defined as a set of graphs, each constituted by a set of nodes and link, eventually tagged with some intensity attribute, that have been established during a defined time slot T of the interval of observation. The sequence of the n time slot started at time t0 pretend to report the dynamical relational evolution that the network is supposed to represent. There are some approaches to dynamic network that take into account possible resilience of links as an intensity attribute. In fact, the process of time partitioning can be tricky, dynamic features that appear relevant at some time slot durations can in fact be insignificant look at other time spans. There exist ways to circumvent this problem taking into account the kind of relation the network represents. For example normalizing the duration of the interplay through an inverse exponential function of time slot duration can avoid some dependence on value of T.