Abstract: Systems and methods for conducting reliable transactions 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 and/or a security assessment may be automatically published to other qualifying members of the community based on connectivity values. The other qualifying members may then be given the opportunity to participate in the same transaction or access the same application to initiate their own transaction, or to take action based on information about the transaction and/or security assessment. These transactions may also be based on virtual and/or electronic currencies.

Abstract: A system is provided wherein a processor in a distributed network system determines a network connectivity value associated with a computer in a network. The connectivity value is determined as a function of an aggregation of path scores of paths between the computer and a mobile device and also as a function of at least one of the path scores. Determining the connectivity value may be accomplished by distributing the tasks to distributed processors within one or more of a plurality of clusters of processors. The processors perform their respective computing task before reporting results. A virtual marker system for use within the network may be provided. Markers in the marker system may be provided based on network connectivity value.

Abstract: A system is provided wherein a processor in a distributed network system determines a network connectivity value associated with a computer in a network. The connectivity value is determined as a function of an aggregation of path scores of paths between the computer and a mobile device and also as a function of at least one of the path scores. Determining the connectivity value may be accomplished by distributing the tasks to distributed processors within one or more of a plurality of clusters of processors. The processors perform their respective computing task before reporting results. A virtual marker system for use within the network may be provided. Markers in the marker system may be provided based on network connectivity value.

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 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, 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 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: A cluster of mobile devices is provided for performing parallel computation of network connectivity through distribution of subprocesses to a plurality of the mobile devices. Network nodes representing computation devices are connected via paths comprising links between nodes. The connectivity of such nodes may be used to determine potential interactions between the nodes. Connectivity values may be based on the frequency or type of interactions between community members. A server may distribute subprocesses for computation of connectivity to various mobile devices in the cluster of devices and receive the results of the subprocesses. Identification of electronic interaction between two or more computation devices in the network community may result in connectivity values being adjusted upwards or downwards depending on the type of interaction between the computation devices, the content of the interaction, and/or the result of the interaction.

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 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 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 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 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 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 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, 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, 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.