A system, method, and apparatus are provided for automatically establishing an inferred (‘follow’) relationship between a first member and a second member of a user community. Based on passive and/or active signals indicating affinity of the first member for the second member, the system determines whether an inferred connection from the first member to the second member would improve the first member's network within the user community. A potential improvement may be observed if the potential connection would improve some aspect or characteristic of a target or ideal network identified for the first member, and/or if there is more than a threshold level of affinity between the members. The first member's network may be pruned, if necessary, to accommodate a connection to the second member if, for example, the connection would violate a constraint associated with the target or ideal network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: determining a passive affinity of a first member of a user community with a second member of the user community, based on characteristics of one or more other members of the user community connected to both the first member and the second member; observing one or more interactions by the first member with content associated with the second member; determining whether an inferred connection from the first member to the second member would improve a first connection network, of the first member, within the user community; and modifying the first connection network to include an inferred connection to the second member, without modifying a second connection network of the second member. 2. The method of claim 1, wherein determining whether an inferred connection from the first member to the second member would improve the first connection network comprises: identifying a member type of the first member; obtaining a set of characteristics of a target network associated with the member type; and determining whether addition of the inferred connection to the first connection network will cause corresponding characteristics of the first connection network to more closely match the set of characteristics of the target network. 3. The method of claim 2, wherein identifying a member type of the first member comprises: examining multiple attributes of a profile of the first member; wherein the member type is one of: professional; professor; student; sales; public face; and thought leader. 4. The method of claim 2, wherein the set of characteristics comprises one or more of: a number of connections within the target network; a number of inferred connections within the target network; a maximum number of connections, within the target network, to members having a predetermined member type; and a minimum number of connections, within the target network, to members having diversity within a predetermined profile attribute. 5. The method of claim 1, wherein determining whether an inferred connection from the first member to the second member would improve the first connection network comprises: for each of the observed interactions by the first member with content associated with the second member: assigning a value to the content interaction; calculating a product of the assigned weight and a weight corresponding to the interaction; combining the calculated products to determine an active affinity of the first member for the second member. 6. The method of claim 5, wherein determining whether an inferred connection from the first member to the second member would improve the first connection network further comprises: combining the active affinity and the passive affinity to yield a value; and when the combined value exceeds a threshold, determining that the inferred connection from the first member to the second member would improve the first connection network. 7. The method of claim 1, wherein observing one or more interactions of the first member with content associated with the second member comprises: receiving notification of a first interaction of the first member with a first content item with which the second member previously interacted; and determining a content category associated with the first interaction. 8. The method of claim 7, wherein the content category is one of: a content creation category; a content curation category; a content consumption category; and a profile update category. 9. The method of claim 1, further comprising: based on member types of the first member and the second member, characterizing the inferred connection as one of the following types: colleague; competitor; mentor; poaching; and business partner. 10. An apparatus, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to: determine a passive affinity of a first member of a user community with a second member of the user community, based on characteristics of one or more other members of the user community connected to both the first member and the second member; observe one or more interactions by the first member with content associated with the second member; determine whether an inferred connection from the first member to the second member would improve a first connection network, of the first member, within the user community; and modify the first connection network to include an inferred connection to the second member, without modifying a second connection network of the second member. 11. The apparatus of claim 10, wherein determining whether an inferred connection from the first member to the second member would improve the first connection network comprises: identifying a member type of the first member; obtaining a set of characteristics of a target network associated with the member type; and determining whether addition of the inferred connection to the first connection network will cause corresponding characteristics of the first connection network to more closely match the set of characteristics of the target network. 12. The apparatus of claim 11, wherein identifying a member type of the first member comprises: examining multiple attributes of a profile of the first member; wherein the member type is one of: professional; professor; student; sales; public face; and thought leader. 13. The apparatus of claim 11, wherein the set of characteristics comprises one or more of: a number of connections within the target network; a number of inferred connections within the target network; a maximum number of connections, within the target network, to members having a predetermined member type; and a minimum number of connections, within the target network, to members having diversity within a predetermined profile attribute. 14. The apparatus of claim 10, wherein determining whether an inferred connection from the first member to the second member would improve the first connection network comprises: for each of the observed interactions by the first member with content associated with the second member: assigning a value to the content interaction; calculating a product of the assigned weight and a weight corresponding to the interaction; combining the calculated products to determine an active affinity of the first member for the second member. 15. The apparatus of claim 14, wherein determining whether an inferred connection from the first member to the second member would improve the first connection network further comprises: combining the active affinity and the passive affinity to yield a value; and when the combined value exceeds a threshold, determining that the inferred connection from the first member to the second member would improve the first connection network. 16. The apparatus of claim 10, wherein observing one or more interactions of the first member with content associated with the second member comprises: receiving notification of a first interaction of the first member with a first content item with which the second member previously interacted; and determining a content category associated with the first interaction. 17. The apparatus of claim 16, wherein the content category is one of: a content creation category; a content curation category; a content consumption category; and a profile update category. 18. The apparatus of claim 10, wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to: based on member types of the first member and the second member, characterizing the inferred connection as one of the following types: colleague; competitor; mentor; poaching; and business partner. 19. A system, comprising: one or more processors; and an inference module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to: determine a passive affinity of a first member of a user community with a second member of the user community, based on characteristics of one or more other members of the user community connected to both the first member and the second member; observe one or more interactions by the first member with content associated with the second member; and determine whether an inferred connection from the first member to the second member would improve a first connection network, of the first member, within the user community; and a connection module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to: modify the first connection network to include an inferred connection to the second member, without modifying a second connection network of the second member. 20. The system of claim 19, wherein determining whether an inferred connection from the first member to the second member would improve the first connection network comprises: identifying a member type of the first member; obtaining a set of characteristics of a target network associated with the member type; and determining whether addition of the inferred connection to the first connection network will cause corresponding characteristics of the first connection network to more closely match the set of characteristics of the target network.