Data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations

An electronic device comprising:

• a data storage configured to store one or more data sets associated with a plurality of individuals;
• a feature discovery engine configured to:
• determine one or more features of the one or more data sets;
• a semi-supervised engine configured to:
• determine similarities among the plurality of individuals, based on the determined features;
• and
• generate data representing a graph having a plurality of nodes, wherein the nodes of the graph are associated with the plurality of individuals, edges between the nodes represent the determined similarities, wherein the nodes include weighted nodes that are associated with a first subset of the plurality of individuals and unweighted nodes;
• a label propagation engine configured to:
• applying a weight to the unweighted nodes of the graph, wherein the weights are based on an influence exerted by the weighted nodes on the unweighted nodes;
• and
• an evaluation engine configured to provide output associated with the graph and the applied weights.

A method performed by one or more processors and comprising:

• obtaining, from one or more data sources, one or more data sets associated with a plurality of individuals;
• determining features associated with the plurality of individuals;
• determining similarities among the plurality of individuals, based on the determined features;
• generating data representing a graph having a plurality of nodes, wherein the nodes of the graph are associated with the plurality of individuals, edges between the nodes represent the determined similarities, wherein the nodes include weighted nodes that are associated with a first subset of the plurality of individuals and unweighted nodes;
• applying a weight to the unweighted nodes of the graph, wherein the weights are based on an influence exerted by the weighted nodes on the unweighted nodes;
• providing output associated with the graph and the applied weights.