Abstract: Methods and systems for measuring relatedness, known as net affinity, between any two arbitrary nodes, known as entities, in a graph is provided. According to one embodiment, unprocessed data about entities is collected from various data sources. The unprocessed data is transformed to a graph with entities as nodes and corresponding direct affinities between them as edges. Depending on relationship between corresponding nodes, the corresponding edges are at least one of non-directed, directed and temporal. The graph is simulated as an electrical circuit with non-directed edges as a conventional resistor. The directed edges are simulated as at least one of a conventional resistor coupled with a forward-biased ideal diode in direction of the directed edges. The temporal edges are simulated with a time-delay circuit involving, at least in part, a capacitor. Further, relatedness between all pairs of nodes is calculated in accordance with the rules of electrical circuit topology.

Abstract: Methods and systems for measuring relatedness, known as net affinity, between any two arbitrary nodes, known as entities, in a graph is provided. According to one embodiment, unprocessed data about entities is collected from various data sources. The unprocessed data is transformed to a graph with entities as nodes and corresponding direct affinities between them as edges. Depending on relationship between corresponding nodes, the corresponding edges are at least one of non-directed, directed and temporal. The graph is simulated as an electrical circuit with non-directed edges as a conventional resistor. The directed edges are simulated as at least one of a conventional resistor coupled with a forward-biased ideal diode in direction of the directed edges. The temporal edges are simulated with a time-delay circuit involving, at least in part, a capacitor. Further, relatedness between all pairs of nodes is calculated in accordance with the rules of electrical circuit topology.

Abstract: Methods and systems for recommending limited, personalized and relevant list of prospects to enterprises, in a configurable, automated, scalable and machine-learnt way. According to one embodiment, raw data about potential prospects across diverse areas is collected from various data sources. The raw data is transformed to variables containing values in a binary format, also known as interests, in accordance with a predetermined set of rules. An interest graph is created with the interests as nodes and affinity between them as edges and net affinities are calculated and stored in an interest table. The user's requirements are understood through user input and a set of user-relevant interests is captured and extended with additional similar interests from the interest table. Multiple scores are calculated for each of the potential prospects based on this set of interests. A net score for each potential prospect is calculated and highest potential prospects are finally recommended.

Abstract: A method of recommending limited choices which are personalized and relevant to a customer in real-time, in which steps thereof are implemented by a computer, the method comprising: receiving data across different categories of products; configuring a taste graph based on the received data; calculating a net affinity between any two products in the taste graph and exporting them as taste scores to a taste table; understanding the customer based on context and behavior from different customer information sets; generating scores for the different customer information sets; determining different weightages for each score to calculate a net score associated with each product for the customer; and recommending limited choices which are personalized and relevant to the customer in real-time based on the descending order of the value of the net score associated with the product.