Abstract: Various embodiments of the present disclosure provide graph-based techniques for generating granular predictive classifications for entities in a predictive domain. The graph-based techniques include generating a network graph for an entity or entity class based on a plurality of interaction data objects for the entity. The network graph includes a plurality of nodes and a plurality of edges. Each node corresponds to a particular interaction code of at least one of the plurality of interaction data objects. Each edge connects a node pair that is associated with a particular interaction data object. The nodes and edges are weighted to enable the clustering of the network graph for an entity class. An entity network graph may be compared to node clusters of a class network graph to generate a behavior based predictive classification.

Abstract: Various embodiments of the present disclosure provide graph-based techniques for generating granular predictive classifications for entities in a predictive domain. The graph-based techniques include generating a network graph for an entity or entity class based on a plurality of interaction data objects for the entity. The network graph includes a plurality of nodes and a plurality of edges. Each node corresponds to a particular interaction code of at least one of the plurality of interaction data objects. Each edge connects a node pair that is associated with a particular interaction data object. The nodes and edges are weighted to enable the clustering of the network graph for an entity class. An entity network graph may be compared to node clusters of a class network graph to generate a behavior based predictive classification.

Abstract: Various embodiments of the present disclosure provide graph-based techniques for generating granular predictive classifications for entities in a predictive domain. The graph-based techniques include generating a network graph for an entity or entity class based on a plurality of interaction data objects for the entity. The network graph includes a plurality of nodes and a plurality of edges. Each node corresponds to a particular interaction code of at least one of the plurality of interaction data objects. Each edge connects a node pair that is associated with a particular interaction data object. The nodes and edges are weighted to enable the clustering of the network graph for an entity class. An entity network graph may be compared to node clusters of a class network graph to generate a behavior based predictive classification.