Abstract: The method uses predictive analysis to determine a model based on past data including a first social network built between communicating entities for a first observation period and behavioral centrality measures derived from behavioral data observed in a following time period. The model thus determined is then applied to a second social network built for a second observation period more recent than the first one. This provides predicted behavioral centrality measures for a future period, which can be used to perform an efficient selection of entities in the target, which may maximize virality with respect to the specific behavior of interest.

Abstract: The method uses predictive analysis to determine a model based on past data including a first social network built between communicating entities for a first observation period and behavioral centrality measures derived from behavioral data observed in a following time period. The model thus determined is then applied to a second social network built for a second observation period more recent than the first one. This provides predicted behavioral centrality measures for a future period, which can be used to perform an efficient selection of entities in the target, which may maximize virality with respect to the specific behavior of interest.

Abstract: A method of and a system for generating a dataset from data stored in at least one data base, for input into an analytical model. The method comprises the steps of: defining a time stamped population comprising a plurality of tuples, each tuple comprising an entity identifier of an entity for analysis, and at least one reference time stamp associated with the corresponding entity identifier; and creating a dataset by generating at least one time dependent attribute value for each entity identifier from data associated with said entity identifier in the at least one database, the or each time dependent attribute value representing a time dependent parameter of the corresponding entity identifier and being generated according to a corresponding attribute definition, wherein the or each time dependent attribute value is generated as a function of the corresponding time stamp.

Abstract: A system and method are disclosed for generating a robust model of a system that selects a modeling function. The modeling function has a set of weights and the modeling function has a complexity that is determined by a complexity parameter. For each of a plurality of values of the complexity parameter an associated set of weights of the modeling function is determined such that a training error is minimized for a training data set. An error for a cross validation data set is determined for each set of weights associated with one of the plurality of values of the complexity parameter and the set of weights associated with the value of the complexity parameter is selected that best satisfies a cross validation criteria. Thus, the selected set of weights used with the modeling function provides the robust model.

Abstract: A system and method are disclosed for generating a robust model of a system that selects a modeling function. The modeling function has a set of weights and the modeling function has a complexity that is determined by a complexity parameter. For each of a plurality of values of the complexity parameter an associated set of weights of the modeling function is determined such that a training error is minimized for a training data set. An error for a cross validation data set is determined for each set of weights associated with one of the plurality of values of the complexity parameter and the set of weights associated with the value of the complexity parameter is selected that best satisfies a cross validation criteria. Thus, the selected set of weights used with the modeling function provides the robust model.