System for discovering hidden correlation relationships for risk analysis using graph-based machine learning

A method, comprising:

• accessing data associated with known correlation relationships that exist among a plurality of entities, wherein the data comprises information associated with the known correlation relationships and an indication of a strength of the known correlation relationships;
• generating a graph based on the known correlation relationships and the strength of the known correlation relationships, wherein vertexes of the graph correspond to the plurality of entities and edges of the graph correspond to the known correlation relationships;
• computing, based on the graph, a set of features and corresponding labels for the plurality of entities, wherein the set of features and the corresponding labels are computed based on converting the graph to a reduced graph by removing an edge of the edges of the graph representing a type of relationship between a pair of vertexes of the vertexes that is sought to be revealed from being hidden, wherein at least one feature of the set of features is computed by utilizing a proximity measure indicating a measure of how close each vertex of the pair of vertexes are to each other, wherein the proximity measure indicates a number of paths between the pair of vertexes and a sum of a weighted path length;
• training a machine learning model using the computed set of features and the corresponding labels;
• determining, by utilizing instructions from a memory that are executed by a processor, a hidden correlation relationship between a first entity of the plurality of entities and at least one additional entity of the plurality of entities by utilizing the machine learning model trained with the computed set of features;
• and
• displaying, on a user device, the hidden relationship to a user using the user device to assist the user in making a decision regarding the first entity.

A non-transitory computer-readable device comprising instructions, which when loaded and executed by a processor, cause the processor to perform operations comprising:

• receiving data associated with known correlation relationships that exist among a plurality of entities, wherein the data comprises information associated with the known correlation relationships and an indication of a strength of the known correlation relationships;
• generating a graph based on the known correlation relationships and the strength of the known correlation relationships, wherein vertexes of the graph correspond to the plurality of entities and edges of the graph correspond to the known correlation relationships;
• computing, based on the graph, a set of features and corresponding labels for the plurality of entities, wherein the set of features and the corresponding labels are computed based on converting the graph to a reduced graph by removing an edge of the edges of the graph representing a type of relationship between a pair of vertexes of the vertexes that is sought to be revealed from being hidden, wherein at least one feature of the set of features is computed by utilizing a proximity measure indicating a measure of how close each vertex of the pair of vertexes are to each other, wherein the proximity measure indicates a number of paths between the pair of vertexes and a sum of a weighted path length;
• training a machine learning model using the computed set of features and the corresponding labels;
• determining a hidden correlation relationship between a first entity of the plurality of entities and at least one additional entity of the plurality of entities by utilizing the machine learning model trained with the computed set of features;
• and
• displaying, on a user device, the hidden correlation relationship to a user using the user device to assist the user in making a decision regarding the first entity.