Ensemble of machine learning engines coupled to a graph structure that spreads heat

An apparatus comprising:

• a first interface configured to receive transaction data;
• a graph module configured to store and update a graph using the transaction data, the graph comprising nodes and edges, wherein each node corresponds to an entity type, and wherein each edge represents a relationship between two nodes;
• and
• an ensemble of machine learning sub-engines programmed to perform steps comprising:
• training a machine learning model of a machine learning sub-engine of the ensemble using a corpus, wherein the corpus comprises a training data and a test data;
• classifying a plurality of nodes in the graph based on the machine learning model, by setting a classification attribute of a first node and a second node of the plurality of nodes to one of a plurality of classifications;
• and
• inserting an edge in the graph between the first node and the second node in response to the machine learning model detecting a pattern.

A non-transitory computer readable medium storing computer-executable instructions that, when executed by a graphics processing unit, cause an ensemble of machine learning sub-engines to:

• train a machine learning model of the ensemble of machine learning sub-engines using a corpus, wherein the corpus comprises a training data and a test data;
• classify a plurality of nodes in a graph, which comprises nodes and edges and is stored in computer memory, based on the machine learning model, by setting a classification attribute of a first node and a second node of the plurality of nodes to one of a plurality of classifications;
• and
• insert an edge in the graph between the first node and the second node in response to the machine learning model detecting a pattern, wherein the first node corresponds to a first entity type and the second node does not correspond to a second entity type.