Machine learning and prediction using graph communities

A computer comprising:

• a processor;
• a network interface;
• and
• a non-transitory computer readable medium, the computer readable medium comprising code, executable by the processor, instructing the computer to:
• a) receive interaction data for a plurality of known interactions between resource providers and users;
• b) create a topological graph based on the plurality of known interactions, the topological graph comprising nodes and edges;
• c) determine a plurality of communities to form a predictive model, each community comprising a dense collection of nodes connected by edges;
• d) receive a request for a prediction;
• e) apply the request to the predictive model by identifying a community in the plurality of communities corresponding to the request;
• f) determine a node within the identified community;
• and
• g) provide information regarding the node as the requested prediction,
• wherein the instruction to determine a plurality of communities to form a predictive model comprises a method that includes the steps of:
• h) computing a weight of each edge in the topological graph;
• i) computing a weight of each node in the topological graph based on the computed weights of each of the edges;
• j) generating a queue comprising the nodes in decreasing order by weight;
• k) selecting a seed node from the queue, the seed node being a highest weighted node in the queue;
• l) generating a community comprising the seed node;
• m) calculating an interaction probability for each candidate node not included in the community, the interaction probability being a probability of a node not included in the community interacting with a node included in the community;
• n) determining a highest priority node based on the interaction probabilities calculated in step m);
• o) determining if the highest priority node meets predefined criteria;
• p) adding the highest priority node to the community if it is determined at step o) that the highest priority meets the predefined criteria;
• q) repeating steps m) through p) until it is determined at step o) that the highest priority node does not meet the predefined criteria;
• r) outputting the community, the community comprising a unique identifier for the community and identifiers for the nodes included in the community;
• s) removing from the queue, the nodes included in the community;
• and
• t) repeating steps k) through s) until the queue is empty, each community outputted at step r) being a community in the plurality of communities that forms the predictive model.

A user device comprising:

• a processor;
• a network interface;
• one or more input elements;
• a display;
• and
• a non-transitory computer readable medium, the non-transitory computer readable medium comprising code, executable by the processor, instructing the user device to:
• receive, from a user of the user device, one or more inputs relating to a request for a prediction, the one or more inputs being received and processed by the user device via the one or more input elements;
• generate the request for the prediction based on the one or more inputs from the user;
• send, via the network interface, the request for the prediction to one or more computers;
• receive, via the network interface, data for the requested prediction, the data for the prediction being generated by the one or more computers by applying the request to a predictive model generated from interaction data of the user and other users of other user devices;
• and
• display the data for the requested prediction to the user using the display, wherein the predictive model, generated from the interaction data of the user and the other users, is generated using a method comprising:
• h) computing a weight of each edge in a topological graph generated from the interaction data,
• i) computing a weight of each node in the topological graph based on the computed weights of each of the edges;
• j) generating a queue comprising the nodes in decreasing order by weight;
• k) selecting a seed node from the queue, the seed node being a highest weighted node in the queue;
• l) generating a community comprising the seed node;
• m) calculating an interaction probability for each candidate node not included in the community, the interaction probability being a probability of a node not included in the community interacting with a node included in the community;
• n) determining a highest priority node based on the interaction probabilities calculated in step m);
• o) determining if the highest priority node meets predefined criteria;
• p) adding the highest priority node to the community if it is determined at step o) that the highest priority meets the predefined criteria;
• q) repeating steps m) through p) until it is determined at step o) that the highest priority node does not meet the predefined criteria;
• r) outputting the community, the community comprising a unique identifier for the community and identifiers for the nodes included in the community;
• s) removing from the queue, the nodes included in the community;
• and
• t) repeating steps k) through s) until the queue is empty, each community outputted at step r) being a community in a plurality of communities that forms the predictive model used to apply the request from the user device and deliver the requested prediction to the user.