Abstract: A system and method for training relational networks. A method includes applying a self-organizing map (SOM) to training data in order to create a visualization. The SOM is a neural network configured to transform relationships between data items. The visualization has a lower dimensionality than the training data. The method also includes training machine learning models of a generative relational network (GRN) based on the visualization, where the GRN includes sets of nodes having respective machine learning models among the machine learning models of the GRN and the sets of nodes include a set of dominance factor nodes and a set of evolution of internal component nodes. The set of dominance factor nodes defines a dominance factor based on change intensity and change frequency, and the set of evolution of internal component nodes defines evolution with respect to changes determined based on values of the dominance factor over time.

Abstract: A system and method for training relational networks. A method includes applying a self-organizing map (SOM) to training data in order to create a visualization. The SOM is a neural network configured to transform relationships between data items. The visualization has a lower dimensionality than the training data. The method also includes training machine learning models of a generative relational network (GRN) based on the visualization, where the GRN includes sets of nodes having respective machine learning models among the machine learning models of the GRN and the sets of nodes include a set of dominance factor nodes and a set of evolution of internal component nodes. The set of dominance factor nodes defines a dominance factor based on change intensity and change frequency, and the set of evolution of internal component nodes defines evolution with respect to changes determined based on values of the dominance factor over time.

Abstract: A system and method for identifying relationships using relational networks. A method includes applying a generative relational network (GRN) in order to create a model of relationships between entities. The GRN includes multiple sets of nodes, where each set of nodes includes a respective set of machine learning models. The sets of nodes include dominance factor nodes and evolution of internal component nodes, where the dominance factor nodes define a dominance factor based on change intensity and change frequency, and the evolution of internal component nodes define evolution with respect to changes over time. Relationships among the entities are simulated using the model, and at least a portion of the relationships are eliminated for a target interaction based on the simulation results. The remaining relationships are tested with respect to the target interaction in order to identify the target interaction.