Abstract: Systems and methods for social graph data analytics to determine the connectivity between nodes within a community are provided. A user may assign user connectivity values to other members of the community, or connectivity values may be automatically assigned from third parties or based on the frequency of interactions between members. Connectivity values may represent such factors as alignment, reputation, status, and/or influence within a social graph of a network community, or the degree of trust. The paths connecting a first node to a second node may be retrieved, and social graph data analytics may be performed on the retrieved paths. Network connectivity values and/or other social graph data may be outputted to third-party processes and services for use in initiating automatic transactions or making automated network-based or real-world decisions.

Abstract: Systems and methods for social graph data analytics to determine the connectivity between nodes within a community are provided. A user may assign user connectivity values to other members of the community, or connectivity values may be automatically harvested, calculated, or assigned from third parties or based on the frequency of interactions between members of the community. Connectivity values may represent such factors as alignment, reputation, status, and/or influence within a social graph within the network community, or the degree of trust. The paths connecting a first node to a second node may be retrieved, and social graph data analytics may be used to determine a network connectivity value from all or a subset of all of the retrieved paths and/or one or more connectivity statistics value associated with the first node and/or the second node.

Abstract: Systems, devices, and methods are described herein for searching for entities based on trust score and geography. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations and may take into account a variety of factors, including verification data, network connectivity, publicly available information, ratings data, group/demographic information, location data, and transactions to be performed, among others. A user may search for entities within a certain geographic location that meet a desired trust score. The results of the search may be generated for display on a user device, for example, by generating a map that shows the current location of the user device and the identified entities. The search may be filtered by entering an anticipated activity or transaction to be performed or desired by the user, and thereby returning entities that are associated with the activity or transaction.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems and methods for social graph data analytics to determine the connectivity between nodes within a community are provided. A user may assign user connectivity values to other members of the community, or connectivity values may be automatically assigned from third parties or based on the frequency of interactions between members. Connectivity values may represent such factors as alignment, reputation, status, and/or influence within a social graph of a network community, or the degree of trust. The paths connecting a first node to a second node may be retrieved, and social graph data analytics may be performed on the retrieved paths. Network connectivity values and/or other social graph data may be outputted to third-party processes and services for use in initiating automatic transactions or making automated network-based or real-world decisions.

Abstract: Systems and methods for calculating trust scores (trustworthiness) based on social graph statistics are described herein. The trust scores (trustworthiness) may be determined within or between individuals, groups, and/or entities represented by nodes in a social graph or other network. Social graph analytics may be used to determine connectivity statistics for each node in the social graph or network. The trust score may then be calculated by analyzing a number of paths connecting nodes in the network and the connectivity statistics associated with the nodes of the network. This trust score may then be used to make prospective real-world decisions, such as a request for credit or initiating a transaction.

Abstract: Systems and methods for social graph data analytics to determine the connectivity between nodes within a community are provided. A user may assign user connectivity values to other members of the community, or connectivity values may be automatically harvested or assigned from third parties or based on the frequency of interactions between members of the community. Connectivity values may represent such factors as alignment, reputation, status, and/or influence within a social graph of a network community, or the degree of trust. The paths connecting a first node to a second node may be retrieved, and social graph data analytics may be performed on the retrieved paths. For example, a network connectivity value may be determined from all or a subset of all of the retrieved paths.

Abstract: Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and/or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group/demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

Abstract: Systems and methods for social graph data analytics to determine the connectivity between nodes within a community are provided. A user may assign user connectivity values to other members of the community, or connectivity values may be automatically harvested or assigned from third parties or based on the frequency of interactions between members of the community. Connectivity values may represent such factors as alignment, reputation, status, and/or influence within a social graph of a network community, or the degree of trust. The paths connecting a first node to a second node may be retrieved, and social graph data analytics may be performed on the retrieved paths. For example, a network connectivity value may be determined from all or a subset of all of the retrieved paths.

Abstract: Systems and methods for visualizing trust scores in a social graph are described herein. The trust scores may be determined within or between individuals, groups, and/or entities connected in a network. A user may initiate the visualization of connections between themselves and others in the network. Contacts are selected for the visualization of the social graph, and a spatial distribution of the nodes is calculated. Nodes in the visualization of the social graph are rendered based on the calculated spatial distribution. Connections between the nodes may be rendered based on connectivity statistics calculated between the rendered nodes.

Abstract: A system is provided for determining network connectivity between a first node and a second node connected to the first node by at least one path is provided, the system comprising a parallel computational framework comprising a plurality of processors, the system being connected to a data store device, a key-value store device and an application server comprising processing circuitry. The system can be operable to receive a user request for a network connectivity value between a first entity of a network community and a second entity of the network community. The system is also operable to transmit an indication of the user request to a first remote server and receive an indication of the network connectivity value from the first remote server. The system is also operable to automatically determine at least one prospective decision regarding the second entity based on a magnitude of the network connectivity value.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: Systems and methods to determine trust scores and/or trustworthiness levels and/or connectivity are described herein. Trust and/or Trustworthiness and/or connectivity may be determined within, among or between entities and/or individuals. Social analytics and network calculations described herein may be based on user-assigned links or ratings and/or objective measures, such as data from third-party ratings agencies. The trust score may provide guidance about the trustworthiness, alignment, reputation, status, membership status and/or influence about an individual, entity, or group. The systems and methods described herein may be used to make prospective real-world decisions, such as whether or not to initiate a transaction or relationship with another person, or whether to grant a request for credit.

Abstract: Systems and methods are described herein for learning an entity's trust model and risk tolerance. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be combined according to a set of weights which reflect an entity's trust model and risk tolerance. For example, an entity may weight data of a certain type more heavily for certain types of transactions and another type of data more heavily for other transactions. By gathering data about the entity, a system may predict the entity's trust model and risk tolerance and adjust the set of weights accordingly for calculating trust scores. Furthermore, by monitoring how entities adjust weights for different transaction types, default weighting profiles may be created that are customized for specific transaction types. As another example, an entity's trust score, as reported to a requesting entity, may be adjusted based on that requesting entity's own trust model, or how “trusting” the requesting entity is.

Abstract: Systems and methods for calculating trust scores (trustworthiness) based on social graph statistics are described herein. The trust scores (trustworthiness) may be determined within or between individuals, groups, and/or entities represented by nodes in a social graph or other network. Social graph analytics may be used to determine connectivity statistics for each node in the social graph or network. The trust score may then be calculated by analyzing a number of paths connecting nodes in the network and the connectivity statistics associated with the nodes of the network. This trust score may then be used to make prospective real-world decisions, such as a request for credit or initiating a transaction.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems and methods for social graph data analytics and node traversal are described herein. A social graph may comprise two or more nodes that each represents an individual, group, or entity, and links may connect the two or more nodes. A distributed graph storage/computation system may be configured to store node and link elements of one or more network communities in a distributed fashion. For example, the distributed graph storage/computation system may include a cluster registry, one or more node storage clusters, and one or more edge storage clusters. The cluster registry, node storage clusters, and edge storage clusters may each provide functions for providing node and link information and for traversing the social graph.

Abstract: Systems and methods to determine trust scores and/or trustworthiness levels and/or connectivity are described herein. Trust and/or Trustworthiness and/or connectivity may be determined within, among or between entities and/or individuals. Social analytics and network calculations described herein may be based on user-assigned links or ratings and/or objective measures, such as data from third-party ratings agencies. The trust score may provide guidance about the trustworthiness, alignment, reputation, status, membership status and/or influence about an individual, entity, or group. The systems and methods described herein may be used to make prospective real-world decisions, such as whether or not to initiate a transaction or relationship with another person, or whether to grant a request for credit.

Abstract: Systems and methods are described herein for calculating trust score based on crowdsourced information. The trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of an entity in a particular community or in relation to another entity. The trust score may be calculated based on data received from a variety of public and private data sources, including “crowdsourced” information. For example, users may provide and/or comment on attributes, characteristics, features, or any other information about another user. These inputs may serve to both validate the available data as well as provide additional information about the user that may not be otherwise available. The participation of the “crowd” may form a type of validation in itself and give comfort to second-order users, who know that the crowd can spectate and make contributions to the attributes, characteristics, features, and other information.

Abstract: Systems, devices, and methods are described herein for searching for entities based on trust score and geography. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations and may take into account a variety of factors, including verification data, network connectivity, publicly available information, ratings data, group/demographic information, location data, and transactions to be performed, among others. A user may search for entities within a certain geographic location that meet a desired trust score. The results of the search may be generated for display on a user device, for example, by generating a map that shows the current location of the user device and the identified entities. The search may be filtered by entering an anticipated activity or transaction to be performed or desired by the user, and thereby returning entities that are associated with the activity or transaction.