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 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, 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 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 recruiting, counter-terrorism/security, insurance underwriting, sales and marketing improvement, decisioning financial transactions and collections, and social scoring are provided. Machine learning can assign connectivity values to other community members, including individuals, companies, products, brands, cities or neighborhoods, etc. Connectivity values may be automatically harvested from or assigned by third parties or based on the frequency and/or type of interactions between community members. Connectivity values may represent such factors as alignment, reputation within the community, degree of trust, competence at one or more skills, or compatibility with others. The degree and type of connectivity between two entities may be assessed by computing a connectivity value based upon connections between entities and relative or absolute trust, competence and/or compatibility features of the connections.

Abstract: Systems and methods for social graph data analytics to determine the connectivity between nodes within a communit 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, andlor 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 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 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, 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 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 recruiting, counter-terrorism/security, insurance underwriting, sales and marketing improvement, decisioning financial transactions and collections, and social scoring are provided. Machine learning can assign connectivity values to other community members, including individuals, companies, products, brands, cities or neighborhoods, etc. Connectivity values may be automatically harvested from or assigned by third parties or based on the frequency and/or type of interactions between community members. Connectivity values may represent such factors as alignment, reputation within the community, degree of trust, competence at one or more skills, or compatibility with others. The degree and type of connectivity between two entities may be assessed by computing a connectivity value based upon connections between entities and relative or absolute trust, competence and/or compatibility features of the connections.

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 conducting more reliable financial transactions, credit decisions, and security assessments 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 alignment, reputation within the network community, or the degree of trust. Information about a financial transaction initiated by a first member of the community, a credit decision, and/or a security assessment may be automatically published to other qualifying members of the community based on connectivity values.

Abstract: Systems and methods for conducting reliable financial transactions, credit decisions, and security assessments 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 within the network community, or the degree of trust. Information about a financial transaction initiated by a first member of the community, a credit decision, and/or a security assessment may be automatically published to other qualifying members of the community based on connectivity values.

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 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 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.