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 methods, apparatus, systems, computing devices, computing entities, and/or the like for i) initializing a global model with an initial probability distribution that predicts the likelihood of a target classification, and ii) continuously learning from local data sets across a plurality of edge computing entities over time by: a) generating local machine learning models based on the global machine learning model at edge computing entities, b) observing datasets via the local machine learning models, c) aggregating learnings from the local machine learning models, d) updating the global machine learning model to reflect the aggregated learnings, and e) cascading the updated model to propagate the aggregated learnings across the edge computing entities.