US20150106371A1

US20150106371A1 - Generating connection recommendations based on recent connections and connections of close connections

Info

Publication number: US20150106371A1
Application number: US14/515,995
Authority: US
Inventors: Samir M. Shah
Original assignee: LinkedIn Corp
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2013-10-16
Filing date: 2014-10-16
Publication date: 2015-04-16
Also published as: US20150106361A1; US9361006B2; US20150106365A1

Abstract

The present disclosure is directed to methods, systems, and computer program products for rendering member profile suggestions based on data derived from a social graph and activity data derived from a social network service operation on the social graph. A social network service may store member or organization profiles as nodes on the social graph. In response to a recommendation event, the social network service may identify a source profile having direct connections to a first set of profiles. The social network service may then determine a second set of profiles that are directly connected to one or more profiles of the first set of profiles. The profiles may be further associated with connection activity information, and the social network service may select one or more of the profiles from the second set of profiles as recommended profiles for the source profile based on the connection activity information.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. Pat. App. No. 61/891,787, filed Oct. 16, 2013 and titled “GENERATING CONNECTION RECOMMENDATIONS BASED ON RECENT CONNECTIONS AND CONNECTIONS OF CLOSE CONNECTIONS,” the disclosure of which is incorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to information retrieval and processing. More specifically, the present disclosure relates to methods, systems and computer program products for generating connection recommendations based on data derived from a social network service.

BACKGROUND

Online social network services provide members with a mechanism for defining, and memorializing in a digital format, representations of themselves (e.g., member profiles) and their relationships with other people. This digital representation of relationships between members is frequently referred to as a social graph. Many social network services utilize a social graph to facilitate electronic communications and the sharing of information between its users or members. For instance, the relationship between two members of a social network service, as defined in the social graph of the social network service, may determine the access and sharing privileges that exist between the two members. As such, the social graph in use by a social network service may determine the manner in which two members of the social network service can interact with one another via the various communication and sharing mechanisms supported by the social network service.
Some social network services aim to enable friends and family to communicate and share with one another, while others are specifically directed to business users with a goal of facilitating the establishment of professional networks and the sharing of business information. For purposes of the present disclosure, the terms “social network” and “social network service” are used in a broad sense and are meant to encompass services aimed at connecting friends and family (often referred to simply as “social networks”), as well as services that are specifically directed to enabling business people to connect and share business information (also commonly referred to as “social networks” but sometimes referred to as “business networks” or “professional networks”).
With many social network services, members are prompted to provide a variety of personal information, which may be displayed in a member's personal web page. Such information is commonly referred to as “personal profile information”, or simply “profile information”, and when shown collectively, it is commonly referred to as a member's profile. For example, with some of the many social network services in use today, the personal information that is commonly requested and displayed as part of a member's profile includes a member's age (e.g., birth date), gender, contact information, home town, address, the name of the member's spouse and/or family members, a photograph of the member, interests, and so forth. With certain social network services, such as some business network services, a member's personal information may include information commonly included in a professional resume or curriculum vitae, such as information about a person's education, employment history, job skills, professional organizations, and so forth. With some social network services, a member's profile may be viewable to the public by default, or alternatively, the member may specify that only some portion of the profile is to be public by default. As such, many social network services serve as a sort of directory of people to be searched and browsed.

DESCRIPTION OF THE DRAWINGS

Some embodiments of the technology are illustrated by way of example and not limitation in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating various functional components of a suitable computing environment, consistent with some embodiments, for rendering connection information based on social network activities.

FIGS. 2-4 are diagrams illustrating example data models used by the recommendation engine to generate connection recommendations consistent with some embodiments.

FIGS. 5-6 are flow charts illustrating methods for generating connection recommendations, consistent with some embodiments.

FIG. 7 is a block diagram of a machine in the form of a computing device within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed.

DETAILED DESCRIPTION

The present disclosure describes, among other things, methods, systems, and computer program products, which individually provide functionality for rendering member profile suggestions based on a data derived from a social graph and activity data derived from a social network service operation on the social graph.
Example embodiments may include systems and methods to generate connection recommendations based on data derived from social graph data. As is explained in greater detail below, social graph data may include data identifying member profiles in a social network service and member connections between these member profiles. As is also explained in greater detail below, a connection recommendation may be data that suggests a member profile in the social network service that may be of interest to another member profile, where the two members do not have a direct member connection in the social graph.
One type of connection recommendation may be generated based on timing data of member connections in a social graph. That is, some embodiments may generate, for a source member profile, a connection recommendation for member profiles of indirect connections (e.g., second-degree connections, third-degree connections, and so forth) of the source member based on timing data related to member connections in a social graph of the source member. For example, a recommendation engine may identify a first connection path to a first indirect connection of a source member profile. In one case, the first connection path to the first indirect connection may include a member connection between the source member profile and a first direct connection and a member connection between the first direct connection and the first indirect connection.
The recommendation engine may then, in some embodiments, identify a second connection path to a second indirect connection of the source member profile. In one case, the second connection path to the second indirect connection may include a member connection between the source member profile and a second direct connection and a member connection between the second direct connection and the second indirect connection.
The recommendation engine may then select the first indirect connection based on a timing function that compares the first connection path to the second connection path. The timing function may compare the connection paths based on time data assigned to the member connections of the connection path.
A presentation engine may then surface the selected indirect connection to the source member profile as a connection recommendation. The connection recommendation may be configured to cause a client device operated by the source member to display information from the member profile of the selected indirect connection (e.g., name, title, profile image, and the like). The connection recommendation may also be configured to cause the client device to display an interface element that, if activated by the source member, causes the social network service to form a direct connection between the source member and the selected indirect connection.
Another type of connection recommendation may be generated based on common connections shared by member profiles in a social graph. That is, some example embodiments may generate, for a source member profile, a connection recommendation specifying member profiles selected based on a number of common connections shared between indirect connections (e.g., second-degree connections, third-degree connections, and so forth) of the source member and the source member. For example, a recommendation engine may identify a first connection path to a first indirect connection of a source member profile. In one case, the first connection path to the first indirect connection may include a member connection between the source member profile and a first direct connection and a member connection between the first direct connection and the first indirect connection.
The recommendation engine may then, in some embodiments, identify a second connection path to a second indirect connection of the source member profile. In one case, the second connection path to the second indirect connection may include a member connection between the source member profile and a second direct connection and a member connection between the second direct connection and the second indirect connection.
The recommendation engine may then select the first indirect connection based on a shared connection function that compares the number of shared connections along the first connection path to the number of shared connection along the second connection path.
A presentation engine may then surface the selected indirect connection to the source member profile in the form of a connection recommendation. The connection recommendation may be configured to cause a client device to display information from the member profile of the selected indirect connection (e.g., name, title, profile image, and the like). The connection recommendation may also be configured to cause the client device to display an interface element that, if activated by the source member, causes the social network service to form a direct connection between the source member and the selected indirect connection.
Example embodiments may provide many practical applications. For example, some systems and methods may leverage information associated with member connections between members of a social network service in order to provide targeted, actionable information to the members, in order to encourage and/or prompt the members to seek additional connections within the social network service, encourage outside users to join the social network service, and other benefits.
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of different embodiments of the present invention. It will be evident, however, to one skilled in the art, that the present invention may be practiced without all of the specific details.
Other advantages and aspects of the inventive subject matter will be readily apparent from the description of the figures that follows.
FIG. 1 is a block diagram illustrating various components or functional modules of a social network service 100, consistent with some embodiments. As shown in FIG. 1, the front end consists of a user interface module (e.g., a web server) 112, which receives requests from various client-computing devices, and communicates appropriate responses to the requesting client devices. For example, the user interface module(s) 112 may receive requests in the form of Hypertext Transport Protocol (HTTP) requests, or other web-based, application programming interface (API) requests. The application logic layer includes various application server modules 114, which, in conjunction with the user interface module(s) 112, generates various user interfaces (e.g., web pages) with data retrieved from various data sources in the data layer. With some embodiments, individual application server modules 114 are used to implement the functionality associated with various services and features of the social network service. For instance, the ability to generate connection recommendations for a source member may be service (or services) implemented in independent application server modules 114. Similarly, a variety of other applications or services that are made available to members of the social network service will be embodied in their own application server modules 114. For example, with some embodiments, the social network system 110 includes modules that may individually or in combination provide connection recommendations, such as a recommendation engine 116 and a presentation engine 117. The recommendation engine 116 may be a computer-implemented module configured to generate connection recommendations. Example embodiments may use a variety of information to generate the connection recommendations, such as data derived from member connections in a social graph.
The presentation engine 117 may be a computer-implemented module configured to generate user interface elements for interacting with the connection recommendations. For instance, the presentation engine 117 may generate user interface elements that may execute on a client device. For example, the presentation engine 117 may generate user interface elements that, when executed on a client device operated by the source member, display one or more connection recommendations and user interface control elements for interacting with the displayed colleague recommendations.
As shown in FIG. 1, the data layer includes several databases, such as a database 118 for storing profile data. Consistent with some embodiments, when a person initially registers to become a member of the social network service, the person will be prompted to provide some personal information, such as his or her name, age (e.g., birthdate), gender, interests, contact information, home town, address, the names of the member's spouse and/or family members, educational background (e.g., schools, majors, matriculation and/or graduation dates, etc.), employment history, skills, professional organizations, and so on. This information is stored, for example, in the database with reference number 118.
Once registered, a member may invite other members, or be invited by other members, to connect via the social network service. A “member connection, or simply “connection,” may require a bi-lateral agreement by the members, such that both members acknowledge the establishment of the connection. It is to be appreciated that members may “connect” with entities other than member profiles, such as companies, groups, or any other suitable cohort. The various associations and relationships that the members establish with other members, or with other entities represented by date stored in the database 118, are stored and maintained within the social graph, shown in FIG. 1 with reference number 120.
The social network service 100 may provide a broad range of other applications and services that allow members the opportunity to share and receive information, often customized to the interests of the member. For example, with some embodiments, the social network service may include a photo sharing application that allows members to upload and share photos with other members. With some embodiments, members may be able to self-organize into groups, or interest groups, organized around a subject matter or topic of interest. With some embodiments, the social network service 100 may host various job listings providing details of job openings with various organizations.
As members interact with the various applications, services and content made available via the social network service, the members' behavior (e.g., content (e.g., profiles) viewed, links selected, messages sent, etc.) may be monitored and information concerning the member's behavior may be stored, for example, as indicated in FIG. 1 by the database with reference number 122. One type of behavior data that may be stored in database 122 is member activity between a member having one member profile with another member having another member profile. As described above, examples of member activities include activities where one member: visits a profile page of a member, messages the member, saves the member in a contact list, introduces the member to another member profile.
FIG. 2 illustrates an example of a social graph 200 that includes member connections 204A-E and member profiles 202A-F. For clarity of description, the social graph 200 may be the social graph for the member profile 202A. That is, the social graph 200 may be the social graph that illustrates how the member profile 202A is connected to other member profiles within the social network service. The member profiles 202A-F, commonly referred to as nodes, may each represent a member profile of a user of the social network service. As used herein, the term “connection” can mean any node within a member profile's social graph.
The member connections 204A-E may be data and/or logic that represents that two members have initiated a member connection with each other through the social networking service.
As shown in FIG. 2, member profile 202A has “direct” member connections with member profiles 202B and 202D. When a member profile has a direct member connection with another member profile, the member connection between the two member profiles is referred to as a first-degree connection. Thus, member profile 202A has two first-degree connections (e.g., member profile 202B and 202D).
In the social graph 200, it is possible for member profiles to be indirectly connected to other member profiles through first-degree connections (e.g., connections of connections). The number of edges (e.g., member connections) in a minimum path that connects a member profile to another profile is considered the degree of the connection between the member profiles. For example, FIG. 2 shows that the member profiles 202C and 202F are second-degree connections to the member profile 202A because the minimum path from 202A to 202C includes two member connections (204C and 204E), and the minimum path from 202A to 202F also includes two member connections (204C and 204D). For the same reason, it should be appreciated that member profile 202E is also a second-degree connection.
The limit on the number of degrees of separation for member connections that a member profile is allowed is typically dictated by the restrictions and policies implemented by the social networking service.
In some embodiments, the member connections can be unidirectional (e.g., formed by following or subscribing) or bidirectional (e.g., formed by “connecting” or “friending”). It is also not a limitation of this description that two member connections who are deemed “connections” for the purposes of this disclosure are not necessarily connected in real life, but that can be the case.
As described herein, in some example embodiments, the recommendation engine 116 of FIG. 1 generates connection recommendations based on data derived from member connections in a social graph, and surfaces, through the presentation engine 117, selected member profiles in order to facilitate a member connection between the source member and the selected member profiles, among other things. Thus, the recommendation engine 116 and the presentation engine 117 include components that perform algorithmic processes configured to facilitate members connecting with other members. FIGS. 3-4 are diagrams illustrating example data models used by the recommendation engine 116 to generate connection recommendations, consistent with some embodiments.
As illustrated in FIG. 3, the social graph 200 may include connection time data associated with the member connections. In comparison to FIG. 2, the social graph 200 the member connections 204A-E now each include respective connection time data 306A-E. “Connection time data,” as used herein, may include any suitable data that represents a relative timing in which a connection activity occurred. A connection activity may be any event or activity that is associated with one member connecting with and/or attempting to connect with another member, a member viewing or accessing information indicating potential connections (e.g., “people you may know” information), among other things.
Example connection activity include: a member accepting a connection request or invite from another member; a member sending a connection request or invite to another member; a member importing information from an address book or other database or online location that includes information identifying users or people that are associated with the member; a member following another member; a member viewing the member profile or another member or viewing information identifying potential connections, such as potential connections inferred and/or suggested to the member by the social network service 130; and so on.
With continued reference to FIG. 3, the member connections 204A-E each have connection time data (e.g., connection time data 306A-E) that indicate when the corresponding member connection was formed within the social network service. For example, the member profile 202A and the member profile 202B formed the member connection 204C on “1:15:35 PM, Sep. 12, 2013.” In other embodiments, the time data may be expressed using alternative expressions, such as a measurement of length of time from the creation of the connection activity and the current time. The connection time data may be a time stamp field with a value specified by the social network service when the connection activity is created. For example, a member connection data structure may include, among other things, two fields containing the member profiles being connected and a time stamp field to indicate when the two members formed the member connection.
As illustrated in FIG. 4, the social graph 200 may additionally or alternatively include data specifying the number of shared connections member profiles have with a particular member profile. For example, in comparison to FIG. 2, the data model 200 may include common connection activity data 408A-E. “Common connection activity data,” as used herein, may include any suitable data that represents a connection activity that is shared between two member profiles. As described above, a connection activity may be any event or activity that is associated with one member connecting with and/or attempting to connect with another member, a member viewing or accessing information indicating potential connections, among other things (e.g., a member accepting a connection request or invite from another member, a member sending a connection request or invite to another member, a member importing information from an address book or other database or online location that includes information identifying users or people that are associated with the member, a member following another member, a member viewing the member profile or another member or viewing information identifying potential connections, and so on). Accordingly, common connection activity data may characterize when two member profiles share a member activity involving the same member activity. By way of example and not limitation, two member profiles being connected to the same member profile is an example of a common connection activity.
With reference back to FIG. 4, the member profiles 202B-F each have common connection activity data (e.g., common connection activity data 408B-E) that represents a connection activity that is shared with member profile 202A. For example, the member profile 202B shares ‘10’ common connections with member profile 202A, while the member profile 202C shares ‘5’ common connections.
As described herein, the recommendation engine 116 may perform various methods when generating connection recommendations, such as information rendered for members of the social network service 130. FIG. 5 is a flow diagram illustrating an example method 500 for generating connection recommendations based on social network activities, consistent with some embodiments. The method 500 may be performed by the recommendation engine 116 and presentation engine 117, accordingly, is described herein merely by way of reference thereto. However, it will be appreciated that the method 500 may be performed on any suitable hardware. The method 500 may also be performed by operating on the social graph 200 shown in FIGS. 2, 3, and 4, accordingly, is described herein merely by way of reference thereto. However, it will be appreciated that the method 500 may be performed on any suitable data structure.
In operation 502, the recommendation engine 116 may detect a recommendation event. A recommendation event may be an event that indicates that the recommendation engine 116 is to generate a connection recommendation for a source member. In some cases, the recommendation engine 116 may detect the recommendation event through an explicit request through an application programmable interface (e.g., a function call or web-based service request) or based on detecting that the source member profile logged into or otherwise accessed the social network. The recommendation event may include data that specifies a source member profile for the connection recommendation.
At operation 504, responsive to detecting the recommendation event, the recommendation engine 116 may identify a first connection path to a first indirect connection of the source member profile. In one case, the first connection path to the first indirect connection may include a member connection between the source member profile and a first direct connection and a member connection between the first direct connection and the first indirect connection.
At operation 506, the recommendation engine 116 may then, in some embodiments, identify a second connection path to a second indirect connection of the source member profile. In one case, the second connection path to the second indirect connection may include a member connection between the source member profile and a second direct connection and a member connection between the second direct connection and the second indirect connection.
At operation 508, the recommendation engine 116 may then select the first indirect connection based on a timing function that compares the first connection path to the second connection path. The timing function may compare the connection paths based on time data assigned to the member connections of the connection path. In one embodiment, the timing function determines which of the first connection path or the second connection path occurred earlier in time. In another embodiment, the timing function determines which of the first connection path or the second connection path occurred later in time.
At operation 510, a presentation engine may then surface the selected indirect connection to the source member profile.
Thus, the method 500 of FIG. 5 may be performed, in some cases, to generate connection recommendations for a member of the social network service based on how recent the connections between members formed. Another type of connection recommendation may be generated based on common connections shared by member profiles. Example embodiments may generate, for a source member profile, a recommendation for member profiles of indirect connections based on shared connection data related to a number of shared connections between the source member and one or more member connections in a connection path to an indirect connection.
FIG. 6 is a flow diagram illustrating an example method 600 for generating connection recommendations based on social network activities, consistent with some embodiments. The method 600 may be performed by the recommendation engine 116 and presentation engine 117, accordingly, is described herein merely by way of reference thereto. However, it will be appreciated that the method 600 may be performed on any suitable hardware. The method 600 may also be performed by operating on the social graph 200 shown in FIGS. 2, 3, and 4, accordingly, is described herein merely by way of reference thereto. However, it will be appreciated that the method 600 may be performed on any suitable data structure.
The method 600 may begin at operation 602 when the recommendation engine 116 detects a recommendation event. As discussed above, a recommendation event may be an event that indicates that the recommendation engine 116 is to generate a connection recommendation for a source member. In some cases, the recommendation engine 116 may detect the recommendation event through an explicit request through an application programmable interface (e.g., a function call or web-based service request) or based on detecting that the source member profile logged into or otherwise accessed the social network. The recommendation event may include data that specifies a source member profile for the connection recommendation.
At operation 604, the recommendation engine 116 may identify a first connection path to a first indirect connection of a source member profile. In one case, the first connection path to the first indirect connection may include a member connection between the source member profile and a first direct connection and a member connection between the first direct connection and the first indirect connection.
At operation 606, the recommendation engine 116 may then, in some embodiments, identify a second connection path to a second indirect connection of the source member profile. In one case, the second connection path to the second indirect connection may include a member connection between the source member profile and a second direct connection and a member connection between the second direct connection and the second indirect connection.
At operation 608, the recommendation engine 116 may then select the first indirect connection based on a shared connection function that compares the number of shared connections along the first connection path to the number of shared connection along the second connection path. In one embodiment, the shared connection function selects the first connection path or the second connection path based on the first connection path or the second connection path having a greater number of shared connections. In another embodiment, the shared connection function selects the first connection path or the second connection path based on the first connection path or the second connection path having a fewer number of shared connections.
At operation 610, the presentation engine 117 may then surface the selected indirect connection to the source member profile in the form of a member profile recommendation. The member profile recommendation may be configured to cause a client device to display information from the member profile of the selected indirect connection (e.g., name, title, profile image, and the like). The member profile recommendation may also be configured to cause the client device to display an interface element that, if activated by the source member, causes the social network service to form a direct connection between the source member and the selected indirect connection.
The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules, engines, objects or devices that operate to perform one or more operations or functions. The modules, engines, objects and devices referred to herein may, in some example embodiments, comprise processor-implemented modules, engines, objects and/or devices.
Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. The performance of certain operations may be distributed among the one or more processors, not only residing within a single machine or computer, but deployed across a number of machines or computers. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or at a server farm), while in other embodiments the processors may be distributed across a number of locations.
FIG. 7 is a block diagram of a machine in the form of a computer system or computing device within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. In some embodiments, the machine will be a desktop computer, or server computer, however, in alternative embodiments, the machine may be a tablet computer, a mobile phone, a personal digital assistant, a personal audio or video player, a global positioning device, a set-top box, a web appliance, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
The example computer system 700 includes a processor 702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 704 and a static memory 706, which communicate with each other via a bus 708. The computer system 700 may further include a display unit 710, an alphanumeric input device 712 (e.g., a keyboard), and a user interface (UI) navigation device 714 (e.g., a mouse). In one embodiment, the display unit 710, alphanumeric input device 712, and user interface navigation device 714 are a touch screen display. The computer system 700 may additionally include a storage unit 716 (e.g., drive unit), a signal generation device 718 (e.g., a speaker), a network interface device 720, and one or more sensors, such as a global positioning system sensor, compass, accelerometer, or other sensor.
The storage unit 716 includes a machine-readable medium 722 on which is stored one or more sets of instructions and data structures (e.g., instructions 724) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution thereof by the computer system 700, the main memory 704 and the processor 702 also constituting machine-readable media.
While the machine-readable medium 722 is illustrated in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
The instructions 724 may further be transmitted or received over a communications network 726 using a transmission medium via the network interface device 720 utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Wi-Fi® and WiMax® networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
Although some embodiments has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

Claims

What is claimed is:

1. A method comprising:

receiving, with one or more processors, a recommendation event, the recommendation event identifying an activity having been performed by a first member of a social network service;

identifying, from a plurality of member profiles stored in a non-transitory, computer-readable memory in communication with the one or more processors, a first member profile associated with the first member, the plurality of member profiles being represented as corresponding nodes in a social graph;

selecting, with the one or more processors, from the plurality of member profiles, a second member profile indirectly connected to the first member profile by a first plurality of edges in the social graph;

selecting, with the one or more processors, from the plurality of member profiles, a third member profile indirectly connected to the first member profile by a second plurality of edges in the social graph;

selecting, with the one or more processors, first connection activity information associated with the second member profile;

selecting, with the one or more processors, second connection activity information associated with the third member profile;

determining, with the one or more processors, a recommended member profile from the second member profile and the third member profile based on a comparison of the first connection activity information and the second connection activity information; and

providing, with the one or more processors, the recommended member profile in response to the received recommendation event.

2. The method of claim 1, wherein the second member profile is directly connected to a fourth member profile, the fourth member profile being directly connected to the first member profile.

3. The method of claim 2, wherein:

the third member profile is directly connected to a fifth member profile;

the fifth member profile is directly connected to the first member profile; and

the fourth member profile and the fifth member profile are different member profiles.

4. The method of claim 1, wherein the second member profile and the third member profile are each directly connected to a fourth member profile, the fourth member profile being directly connected to the first member profile.

5. The method of claim 1, wherein:

the first connection activity information comprises connection time data associated with a first edge selected from the first plurality of edges;

the second connection activity information comprises connection time data associated with a second edge selected from the second plurality of edges; and

the comparison of the first connection activity and the second connection activity comprises a comparison of the first connection time data with the second connection time data.

6. The method of claim 5, wherein the recommended member profile is determined based on the first connection time data indicating that the first connection activity occurred more recent than the second connection activity.

7. The method of claim 1, wherein;

the first connection activity information comprises a first number of connections shared between the first member profile and the second member profile;

the second connection activity information comprises a second number of connections shared between the first member profile and the third member profile; and

the comparison of the first connection activity and the second connection activity comprises a comparison of the first number of shared connections with the second number of shared connections.

8. A system comprising:

a non-transitory, computer-readable memory storing computer-executable instructions; and

one or more processors in communication with the non-transitory, computer-readable memory that, having executed the computer-executable instructions, are configured to:

receive a recommendation event, the recommendation event identifying an activity having been performed by a first member of a social network service;

identify, from a plurality of member profiles, a first member profile associated with the first member, the plurality of member profiles being represented as corresponding nodes in a social graph;

select, from the plurality of member profiles, a second member profile indirectly connected to the first member profile by a first plurality of edges in the social graph;

select, from the plurality of member profiles, a third member profile indirectly connected to the first member profile by a second plurality of edges in the social graph;

select first connection activity information associated with the second member profile;

select second connection activity information associated with the third member profile; and

determine a recommended member profile from the second member profile and the third member profile based on a comparison of the first connection activity information and the second connection activity information;

provide the recommended member profile in response to the received recommendation event.

9. The system of claim 8, wherein the second member profile is directly connected to a fourth member profile, the fourth member profile being directly connected to the first member profile.

10. The system of claim 9, wherein:

the third member profile is directly connected to a fifth member profile;

the fifth member profile is directly connected to the first member profile; and

11. The system of claim 8, wherein the second member profile and the third member profile are each directly connected to a fourth member profile, the fourth member profile being directly connected to the first member profile.

12. The system of claim 8, wherein:

13. The system of claim 8, wherein the recommended member profile is determined based on the first connection time data indicating that the first connection activity occurred more recent than the second connection activity.

14. The system of claim 8, wherein;

15. A non-transitory, computer-readable medium storing computer-executable instructions thereon that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:

receiving, with the one or more processors, a recommendation event, the recommendation event identifying an activity having been performed by a first member of a social network service;

16. The non-transitory, computer-readable medium of claim 15, wherein the second member profile is directly connected to a fourth member profile, the fourth member profile being directly connected to the first member profile.

17. The non-transitory, computer-readable medium of claim 15, wherein the second member profile and the third member profile are each directly connected to a fourth member profile, the fourth member profile being directly connected to the first member profile.

18. The non-transitory, computer-readable medium of claim 15, wherein:

19. The non-transitory, computer-readable medium of claim 18, wherein the recommended member profile is determined based on the first connection time data indicating that the first connection activity occurred more recent than the second connection activity.

20. The non-transitory, computer-readable medium of claim 15, wherein;