WO2016003512A1 - Détermination d'un type de relation entre entités disparates - Google Patents

Détermination d'un type de relation entre entités disparates Download PDF

Info

Publication number
WO2016003512A1
WO2016003512A1 PCT/US2015/023332 US2015023332W WO2016003512A1 WO 2016003512 A1 WO2016003512 A1 WO 2016003512A1 US 2015023332 W US2015023332 W US 2015023332W WO 2016003512 A1 WO2016003512 A1 WO 2016003512A1
Authority
WO
WIPO (PCT)
Prior art keywords
relationship
groups
members
company
entities
Prior art date
Application number
PCT/US2015/023332
Other languages
English (en)
Inventor
Ke Wang
Songtao Guo
Baoshi Yan
Alex Ching Lai
Original Assignee
Linkedin Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Linkedin Corporation filed Critical Linkedin Corporation
Publication of WO2016003512A1 publication Critical patent/WO2016003512A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/288—Entity relationship models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • FIG. 1 is a user interface diagram illustrating an example of a user interface or web page for a company represented as an entity in a social graph maintained by a social network service, consistent with some embodiments;
  • FIG. 2 is a block diagram illustrating various functional components of a social networking system with a pathfinder module and a communication prioritization module for use with a wide variety of applications and, specifically, for prioritizing communications, consistent with some embodiments of the invention
  • FIG. 3 is a block diagram illustrating an example of a portion of a graph data structure for implementing a social graph, according to some embodiments of the invention.
  • FIG. 4A shows a flowchart for an example method for determining a type of relationship between disparate entities, in accordance with an example embodiment
  • FIG. 4B shows a flowchart for an example method for determining a function of a group, in accordance with an example embodiment
  • FIG. 5 is a user interface diagram illustrating an example of a user interface or web page enabling a company representative to publish a message or status update, consistent with some embodiments of the invention
  • FIG. 6 is a user interface diagram illustrating an example of a user interface or web page having a personalized data feed (or content stream) via which a member of a social network service may receive communication messages and/or status updates, according to some embodiments;
  • FIG. 7 is a table illustrating a non-exhaustive list of associations that may be attributed to an edge connecting two nodes representing entities in the social graph data structure, according to some embodiments of the invention.
  • FIG. 8 is a diagram illustrating an example of a generalized social graph containing nodes representing several different entities having varying entity types including members, companies, and schools;
  • FIG. 9 is an example of a user interface for use with a messaging application that implements a method, consistent with some embodiments of the invention.
  • FIG. 10 is a representation of an example user interface for a team- sharing application, consistent with some embodiments of the invention.
  • FIG. 11 is a block diagram of a machine in the form of a computing device within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein.
  • the present disclosure describes methods, systems, and computer program products that determine a relationship between disparate entities based on, for example, a social graph.
  • the disclosed techniques may be provided as a social network service, and may be used in conjunction with other social network services and techniques, including social graphs, member profiles, data feeds, and social graph path scoring techniques.
  • members of each entity such as employees of a company or corporation, may be segmented into groups according to a variety of attributes. For example, members may be grouped according to location, job function, and/or job skills. A group may be analysed to determine if the group corresponds to a particular department of the entity. For example, members with customer service skills that are located at the same location may be determined to constitute a customer service department.
  • Each pair of departments or groups from different entities are then analysed to determine if there is a relevant relationship between the pair of groups.
  • a relevant relationship between the pair of groups, or sub-units may be indicative of a relevant relationship between the corresponding entities.
  • a relationship may be measured based on a variety of metrics, and the relationship may be considered a relevant relationship if one or more of the metrics exceeds a corresponding threshold.
  • Online or web-based social network services provide their users with a mechanism for defining, and memorializing in a digital format, their relationships with other people. This digital representation of real- world relationships is frequently referred to as a social graph.
  • a social graph This digital representation of real- world relationships is frequently referred to as a social graph.
  • these social network services have matured, many of the services have expanded the concept of a social graph to enable users to establish or define relationships or associations with any number of entities and/or objects in much the same way that users define relationships with other people. For instance, with some social network services and/or with some web-based applications that leverage a social graph that is maintained by a third-party social network service, users can indicate a relationship or association with a variety of real-world entities and/or objects.
  • users may take action to expressly indicate a favorable opinion of, or an interest in, different types of content (e.g., web-based articles, blog postings, books, photographs, videos, audio recordings, music, and so forth).
  • a user's expression of opinion or interest is captured when a user interacts with a particular graphical user interface element, such as a button, which is generally presented in connection with the particular entity or object and frequently labelled in some meaningful way (e.g., "like,” “+1,” “follow”).
  • the personal information that is commonly requested and displayed as part of a member's profile includes a person's age, birthdate, gender, interests, contact information, residential address, home town and/or state, the name of the person's spouse and/or family members, and so forth.
  • 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, the company at which a person is employed, an industry in which a person is employed, a job title or function, an employment history, skills possessed by a person, professional organizations of which a person is a member, and so on.
  • social network services are a rich source of information about people, social network services are an extremely useful tool when performing certain tasks. For example, many people use social network services to search for, and/or browse, member profiles that exhibit various desired characteristics. For instance, a job recruiter may search for persons who have profiles indicating the possession of certain technical skills and educational and professional experiences and backgrounds. Similarly, when someone needs to hire a person employed in a particular profession (e.g., a general contractor, a doctor, a lawyer, a landscaper, a plumber, an investment banker, and so forth), that person may turn to a social network service to identify persons who possess the requisite skills and qualifications. In another scenario, a person may desire to contact someone for the purpose of exploring or proposing the possibility of a particular business arrangement or relationship. Accordingly, the person may use a social network service to identify the appropriate persons to contact.
  • a person employed in a particular profession e.g., a general contractor, a doctor, a lawyer, a landscaper, a plumber, an investment banker,
  • a social graph may be implemented with a specialized graph data structure in which various entities (e.g., people, companies, schools, government institutions, non-profits, and other organizations) are represented as nodes connected by edges, where the edges have different types representing the various associations and/or relationships between the different entities.
  • entities e.g., people, companies, schools, government institutions, non-profits, and other organizations
  • the social graph data structure may be implemented with a graph database. Accordingly, if a member of the social network service with the name Jeffrey Beaner graduated from Princeton University, this particular association may be represented in the social graph data structure by a node representing the member, Jeffrey, being connected via an edge to another node representing the entity or organization, Princeton University, where the particular edge type indicates the specific type of association - in this case, Jeffrey's status as a graduate of Princeton
  • an organization may have a presence within a social graph of a social network service without necessarily having any particular web-based content that is hosted by the social network service.
  • FIG. 1 is a user interface diagram illustrating an example of a user interface or web page for a company represented as an entity in a social graph maintained by a social network service, consistent with some embodiments.
  • the example web page is for a company with the name "Avocado.”
  • a representative of Avocado has established what might be referred to as a company page 130 with the social network service.
  • the company page 130 for Avocado is hosted by the social network service. Accordingly, members of the social network service who may be interested in the company can access the company page 130 for Avocado to view a variety of information about the company.
  • the company page 130 for Avocado may present a brief history of the company as well as an overview of the products and services that the company provides.
  • the company page 130 for Avocado may present information about various job listings for open employment positions with the company, for example, in connection with the "Careers” tab in FIG. 1.
  • the company page 130 may present statistical information about the members of the social network service who are following the company, or who are subscribed to receive messages or status updates on behalf of the company.
  • Such information generally may include the total number of company followers, the total number of new company followers within some predefined number of days (e.g., last seven days), the number of messages or status updates published on behalf of the company within the same predefined number of days, and so forth.
  • the company page 130 may present page statistics, such as the total number of company page 130 views, the number of company page 130 views within some predefined number of days (e.g., last seven days), and/or the number of page or link selections (e.g., clicks) within the same predefined number of days.
  • page statistics such as the total number of company page 130 views, the number of company page 130 views within some predefined number of days (e.g., last seven days), and/or the number of page or link selections (e.g., clicks) within the same predefined number of days.
  • the company page 130 may include various insights about the company as derived from member profile information and the viewing member's social graph. For example, in connection with the "Insights" tab in the example web page of FIG. 1, a viewing member may be presented with information identifying members of the social network service who are employed at Avocado and who have new job titles, or information about members who have recently departed Avocado for a new company. In one example embodiment, information within the company page 130 may be used to prioritize communications. For example, a communication from an employee of a company who is a client of a recipient may be given a higher priority level. Social Graph Connections
  • some of the many tasks people commonly use a social network service to perform are improved by conveying to a user of the service specific information concerning the associations (e.g., relationships and affiliations) that a user, or an entity on whose behalf the user is acting (e.g., a company, group or other organization with which the user is associated), might share in common with another member of the social network service, while the user is performing a particular task.
  • an association of members of the social graph may be utilized to determine a type of relationship between disparate entities, as described more fully below.
  • a social graph may be useful in a number of services.
  • social graphs used by many conventional social network services model only the relationships that exist between people
  • embodiments of the present invention use a social graph that may include not only people, but other types of entities as well.
  • a social graph may include entity types such as companies, educational institutions, groups, and so forth.
  • connection path in the social graph that connects two members may be based on a wide variety of associations between the various entities, including personal relationships between members, a common employment relationship with a particular company, common membership in a group, and so forth. Such connection paths may be utilized to determine a type of relationship between disparate entities, as described more fully below.
  • a social network service may maintain a social graph, implemented as a graph data structure having nodes and edges, where the nodes represent different entities and the edges represent various associations or relationships between entities.
  • the entity types may include people, companies, educational institutions (e.g., schools and universities), and groups (e.g., online groups, or professional organizations), among others.
  • the edges that connect any two nodes (entities) may represent types of associations between the entities, and may therefore depend, in part, on the entities involved.
  • an edge connecting two nodes that represent people may be representative of a specific type of relationship between the two people, including a direct, bilateral connection between the two people.
  • An edge connecting a first node, representing a person, with a second node, representing a company may be representative of an employment relationship (current or previous) between the person and the company.
  • each edge connecting two entities may be assigned an edge score to reflect the strength, or relevance, of the particular association.
  • the social network service may perform an algorithmic process to analyze the social graph and to identify the connection paths that connect the recipient of the communication with the sender of the communication, such as a user or other entity that is a member of the social network service.
  • the connection path or paths that are determined to be strongest, or most relevant, with respect to the communication may then be visually presented to the user, providing the user with important contextual information for completing the task, and/or may be used to determine a type of relationship between disparate entities, as described more fully below.
  • the terms "communication sender” and “communication recipient” are used herein.
  • a communication recipient is the member to whom a communication is addressed
  • a communication sender is the user performing the task of preparing and sending a communication on his or her own behalf, or on behalf of an entity, such as a company, group or other organization.
  • FIG. 2 is a block diagram illustrating various functional components of a social networking system 210 with a pathfinder module 216 and a communication prioritization module 240 for use with a wide variety of applications and, specifically, for determining a type of relationship between disparate entities, consistent with some embodiments of the invention.
  • the social networking system 210 may be generally based on a three-tiered architecture, consisting of a front-end layer, application logic layer, and data layer.
  • each module or engine shown in FIG. 2 may represent a set of executable software instructions and the corresponding hardware (e.g., memory and processor) for executing the instructions.
  • FIG. 2 various functional modules and engines that are not germane to conveying an understanding of the inventive subject matter have been omitted from FIG. 2.
  • additional functional modules and engines may be used with a social network system, such as that illustrated in FIG. 2, to facilitate additional functionality that is not specifically described herein.
  • the various functional modules and engines depicted in FIG. 2 may reside on a single server computer, or may be distributed across several server computers in various arrangements.
  • FIG. 2 depicted in FIG. 2 as a three-tiered architecture, the inventive subject matter is by no means limited to such architecture.
  • the front end consists of a user interface module (e.g., a web server) 212, which may receive requests from various client- computing devices, and communicates appropriate responses to the requesting client devices.
  • the user interface module(s) 212 may receive requests in the form of Hypertext Transport Protocol (HTTP) requests, or other web-based, application programming interface (API) requests.
  • HTTP Hypertext Transport Protocol
  • API application programming interface
  • the client devices may be executing conventional web browser applications or applications that have been developed for a specific platform to include any of a wide variety of mobile devices and operating systems.
  • the data layer may include several databases, including databases for storing data for various entities of the social graph, including member profiles 218, company profiles 220, educational institution profiles 222, and information concerning various online or offline groups 224.
  • the graph data structure may be implemented with a graph database 226, which is a particular type of database that uses graph structures with nodes, edges, and properties to represent and store data.
  • a graph database 226, is a particular type of database that uses graph structures with nodes, edges, and properties to represent and store data.
  • any number of other entities might be included in the social graph, and as such, various other databases may be used to store data corresponding with other entities.
  • a person when a person initially registers to become a member of the social network service, the person may be prompted to provide some personal information, such as his or her name, age (e.g., birth date), gender, interests, contact information, home town, address, the names of the member's spouse and/or family members, educational background (e.g., schools, majors, etc.), current job title, job description, industry, employment history, skills, professional organizations, and so on.
  • This information is stored, for example, in member profiles 218.
  • a member may invite other members, or may be invited by other members, to connect via the social network service.
  • connection may call for a bi- lateral agreement by the members, such that both members acknowledge the establishment of the connection.
  • a member may elect to "follow" another member.
  • the concept of "following" another member typically may be a unilateral operation, and at least with some embodiments, may not call for acknowledgement or approval by the member that is being followed.
  • the member who is following may receive automatic notifications about various activities undertaken by the member being followed.
  • a user may elect to follow a company, a topic, a conversation, or some other entity, which may or may not be included in the social graph.
  • Various other types of relationships that may exist between different entities, and represented in the social graph database 226, are described in connection with FIG. 7.
  • the application logic layer includes various application server modules 214, which, in conjunction with the user interface module(s) 212, may generate various user interfaces (e.g., web pages) with data retrieved from various data sources in the data layer.
  • individual application server modules 214 may be used to implement the functionality associated with various applications, services and features of the social network service.
  • a messaging application such as an email application, an instant messaging application, or some hybrid or variation of the two, may be implemented with one or more application server modules 214.
  • a search engine enabling users to search for and browse member profiles may be implemented with one or more application server modules 214.
  • the application logic layer may include the pathfinder module 216 and the communication prioritization module 240.
  • the pathfinder module 216 may be implemented as a service that operates in conjunction with various application server modules 214.
  • any number of individual application server modules 214 and/or communication prioritization module 240 may invoke the functionality of the pathfinder module 216, to include an application server module 214 associated with a messaging application and/or an application server module 214 associated with an application to facilitate the viewing of member profiles 218.
  • the pathfinder module 216 may be implemented as its own application server module 214 such that it operates as a stand-alone application.
  • the pathfinder module 216 may include or have an associated publicly available API that enables third-party applications to invoke the functionality of the pathfinder module 216.
  • the pathfinder module 216 may take as input parameters that, at a minimum, identify two different nodes corresponding with two entities (e.g., two persons, or a person and a company, etc.) in a social graph that may be implemented with a graph data structure (e.g., social graph database 226). Using the input parameters, the pathfinder module 216 may analyze the social graph database 226 to identify the connection paths in the social graph that connect the two entities, if any exist. With some embodiments, additional input parameters may be provided to the pathfinder module 216 and may be used to refine the connection paths selected for potential presentation to the user. These parameters may include, for example, filtering criterion to include or exclude connection paths having particular entities, or particular entity types, or specific edge types.
  • the pathfinder module 216 may derive a path score for each connection path, for example, by aggregating the individual edge scores for the edges that connect the different nodes included in the connection paths. Finally, the pathfinder module 216 may provide the information corresponding with the connection paths to the application that invoked the pathfinder module 216 so that a visual representation of one or more connection paths may be presented to the user.
  • the pathfinder module 216 may be invoked from a wide variety of applications.
  • the pathfinder module 216 may be invoked to provide a message sender with a visual representation of a connection path between the message sender and a person to whom the message sender has addressed a message (e.g., the message recipient).
  • the pathfinder module 216 may be invoked to provide a message sender with a visual representation of a connection path connecting an entity on whose behalf the message sender is acting (e.g., company, group, or other organization) with a message recipient.
  • the pathfinder module 216 may be invoked to provide a path score corresponding to a connection path connecting a sender of a communication and a recipient of a communication for use in determining a type of relationship between disparate entities.
  • FIG. 3 is a block diagram illustrating an example of a portion of a graph data structure 330 for implementing a social graph, according to some embodiments of the invention.
  • the graph data structure 330 may consist of nodes connected by edges.
  • a node 332 may be connected to a node 336 by means of an edge 334.
  • Each node in the graph data structure 330 may represent an entity in the social graph.
  • any number of entity types may be included in the social graph.
  • the entity types that may exist in one implementation of a social graph that is consistent with an embodiment of the invention are: a person, a company, an educational institution (e.g., college, school or university), and a group (e.g., an online group, hosted by the social network service, or some other third party server system, or, a real- world organization, such as a professional organization.)
  • the edges 334 that connect any two nodes 332, 336 may represent a wide variety of different associations.
  • an edge 334 may represent a relationship, an affiliation, an activity or event, or some other affinity shared in common between two entities.
  • the various associations presented in the table of FIG. 7 represent some of the many associations that may be mapped to the edges 334 of a social graph data structure 330 to indicate the association between entities in a social graph of a social network service, consistent with some embodiments of the invention.
  • members of each entity such as employees of a company or corporation, may be segmented into groups according to a variety of attributes (e.g., member profile attributes and/or characteristics). For example, members may be grouped according to location, job function, and/or job skills as indicated in one or more member profiles. A group of members may be analysed to determine if the group corresponds to a particular department of the entity. For example, members with customer service skills that are located at the same location may be determined to constitute a customer service department. In other examples, clusters of members with sales skills, analytic skills, or engineering skills may be discovered and their corresponding groups may be categorized as sales, analytics, and engineering departments, respectively.
  • attributes e.g., member profile attributes and/or characteristics
  • members may be grouped according to location, job function, and/or job skills as indicated in one or more member profiles.
  • a group of members may be analysed to determine if the group corresponds to a particular department of the entity. For example, members with customer service skills that are located at the same location may be determined
  • Each pair of departments or pair of groups from different entities may then be analysed to determine if there is a relevant relationship between the pair of departments or groups.
  • a relevant relationship between the pair of departments or groups may, in turn, be indicative of a relevant relationship between the corresponding entities.
  • a relationship may be measured based on a variety of metrics, and the relationship may be considered a relevant relationship if one or more of the metrics exceeds a corresponding threshold.
  • only bi-lateral relationships i.e., relationships acknowledged by both people in the relationship, are considered.
  • relationships acknowledged by only one person in the relationship and/or relationships that have not been acknowledged by either person in the relationship are considered.
  • a connection density ratio may be determined by dividing the number of unique connections between members of a first department (such as a department of a first company) and members of a second department (such as a department of a second company) by the maximum number of potential unique connections between members of the two departments.
  • the maximum number of potential unique connections between members of the two departments may be determined by multiplying the count of employees in the first department by the count of employees in the second department.
  • the ratio exceeds a defined threshold, the first and second departments are considered to have a relevant relationship.
  • the ratio increases at a rate above a defined threshold, the first and second departments are considered to have a relevant relationship.
  • a relevant relationship between departments may be, in turn, indicative of a relevant relationship between the entities to which the departments belong. For example, a relevant relationship between a sales department of a first company and an engineering department of a second company may indicate that the second company is a customer of the first company.
  • a business partner relationship may be inferred, for example, by entities that share industry type, product (similar or complementary), and the like.
  • a competitor relationship between two entities may be determined based on second degree connections.
  • two entities such as companies, that produce similar products in the same industry and that both have a high connection density ratio (a connection density ratio that exceeds a defined threshold) with the same set of customers may be labelled as competitors.
  • a financial relationship between two entities may be determined based on first and/or second degree connections.
  • the type or nature of a relationship between members may be analysed and used in determining if a relevant relationship between the departments or groups exists. For example, in determining relationships between companies, relationships between members that are labelled or determined to be working relationships may be weighted more heavily than friendship relationships. In another example, in determining relationships between civic organizations, relationships between members that are labelled or determined to be friendship relationships may be weighted equally with relationships that are labelled or determined to be business relationships.
  • certain member relationships may be included, excluded, or weighted differently in computing a relationship metric. For example, if a member moves from one entity to another (such as changes employment from a first company to a second company), the member's relationships may be processed differently. In this instance, the business relationships to other members in the first company may be excluded from processing, may be weighted differently, may be changed to a different type of relationship (e.g., "former business" relationship), and the like since the relationship may be only the result of the member's employment at the first company and may not be indicative of a general relationship between the companies. In this case, the friend relationships to other members in the first company may be treated no differently after the employment change. In other examples, member relationships may be included, excluded, or weighted differently in computing a relationship metric based on a length of time of a relationship, a transition of a relationship, and the like.
  • FIG. 4A shows a flowchart for an example method 400 for determining a type of relationship between disparate entities, in accordance with an example embodiment.
  • an entity is selected (operation 404).
  • Members of the selected entity such as employees of a company or corporation, are segmented into groups according to one or more attributes (operation 408).
  • members may be grouped according to location, job function, and/or job skills.
  • the attributes may be obtained, for example, from member profiles of a social networking service.
  • a group of segmented members may be selected (operation 412) and common attributes of the selected group may be identified and/or analysed to determine an identity of the group (operation 416), as described more fully below in conjunction with FIG. 4B.
  • the groups may be analysed to determine if there is a predominate characteristic of the group's members, such as customer service skills. Members with customer service skills that are located at the same location may be determined to constitute a customer service department.
  • clusters of members with sales skills, analytic skills, or engineering skills may be discovered and their corresponding groups may be categorized as sales, analytics, and engineering departments, respectively.
  • a test may be performed to determine if all groups of the selected entity have been analyzed (operation 420). If all groups of the selected entity have not been analyzed, the method 400 may proceed with operation 412. If all groups of the selected entity have been analyzed, a test may be performed to determine if all entities have been selected (operation 424). If all entities have not been selected, the method 400 may proceed with operation 404; otherwise, a group from a first entity and a group from another entity may be selected (operation 428). A relationship between the selected groups may be analysed and a relationship metric may be generated (operation 432). The analysis may determine if there is a relevant relationship between the selected groups and therefore a relevant relationship between the different entities.
  • a relationship may be measured based on a variety of metrics, and the relationship may be considered a relevant relationship if one or more of the metrics exceeds a corresponding threshold.
  • a connection density ratio may be determined by dividing the number of unique connections between each member of a first department (such as a department of a first company) and each member in a second department (such as a department of a second company) by the maximum number of potential unique connections between members of the two departments. The maximum number of potential unique connections between members of the two department may be determined by multiplying the count of employees in the first department by the count of employees in the second department. If the ratio exceeds a defined threshold, such as 30%, the first and second departments may be considered to have a relevant relationship.
  • the relevant relationship between departments may, in turn, be indicative of a relevant relationship between the entities to which the departments belong.
  • a test may be performed to determine if the relationship is a relevant relationship. For example, a test may be performed to determine if the relationship metric exceeds a threshold value (operation 436). If the relationship metric does not exceed the threshold value, the method 400 may proceed with operation 428; otherwise, the relevant relationship may be analysed to determine the type or nature of the relationship between the selected groups and/or corresponding entities and the relationship may be labelled accordingly
  • a relevant relationship between a sales department of a first company and an engineering department of a second company may indicate that the second company is a customer of the first company.
  • the method 400 may then end.
  • operation 432 may be performed using a binary classifier that determines, for example, whether two business units have a relationship.
  • operation 440 may be implemented with a multi-class classifier that determines the type of relationship, such as customer, competitor, business partner, industrial partner, and the like, between entities.
  • the classifiers may be based, for example, on the nearest neighbor algorithm and may provide a confidence score for each determination.
  • the classifiers can be rule-based or can be based on a statistical machine learning model that is trained based on known relationships.
  • the input to the classifier are various types of features and the output is one or more types of relationships.
  • features include social-centric features (such as first degree and second degree connection density, and the like), business-centric features (such as business industry, product type, and the like), member-centric features (such as member title, member position, and the like), behaviour-centric features (such as an article posted about other businesses, a team introduction to another group, and the like), and content-centric features (such as a post that discloses a type of relationship between two business entities).
  • social-centric features such as first degree and second degree connection density, and the like
  • business-centric features such as business industry, product type, and the like
  • member-centric features such as member title, member position, and the like
  • behaviour-centric features such as an article posted about other businesses, a team introduction to another group, and the like
  • content-centric features such as a post that discloses a type of relationship between two business entities.
  • FIG. 4B shows a flowchart for an example method 450 for determining a function of a group, in accordance with an example embodiment.
  • a profile of a member is selected (operation 454) and the member's job function and/or job skill is obtained from the member profile (operation 458).
  • a test is then performed to determine if a group exists that corresponds to the obtained job function and/or job skill (operation 462). If a group exists that corresponds to the obtained job function and/or job skill, the method 450 proceeds with operation 470; otherwise, a group that corresponds to the obtained job function and/or job skill is created (operation 466).
  • the selected member is assigned to the group that corresponds to the obtained job function and/or job skill.
  • a test is then performed to determine if all listed job functions and/or job skills for the selected member have been processed (operation 474). If all listed job functions and/or job skills for the selected member have not been processed, the method 450 proceeds with operation 458; otherwise, a test is performed to determine if all member profiles 218 have been processed (operation 478). If all member profiles 218 have not been processed, the method 450 proceeds with operation 454; otherwise, a group is selected for processing (operation 482).
  • a test is performed to determine if the count of members in the group exceeds a predefined threshold (operation 486).
  • the predefined threshold may be, for example, equal to twenty members. If the count of members in the group does not exceed the predefined threshold, the method 450 proceeds with operation 482. If the count of members in the group exceeds the predefined threshold, the group is identified by the job function and/or job skill initially associated with the group (operation 490).
  • a test is performed to determine if all groups have been processed (operation 494). If all groups have not been processed, the method 450 proceeds with operation 482; otherwise the method 450 ends.
  • a data feed or content stream may be known, to those skilled in the art, by a variety of different names, including a “stream,” “status update stream,” “network update stream,” and/or “news feed.” Similarly, skilled artisans may refer to this type of message by many different names, including a “status update,” “tweet,” or simply, and generically, as a message.
  • high priority communications may be identified in a data feed and/or a content feed. For example, as described more fully below, a message that specifies an action by a recipient may be identified in a data feed.
  • the status update when an authorized representative of an organization publishes a status update, the status update may appear in a content stream presented on the web page of the particular organization on whose behalf the status update is being published. Additionally, the status update may appear in a personalized content stream of those members of the social network service who have taken some action to subscribe to receive messages published on behalf of the organization.
  • the company page 130 may present a user interface for a data feed or content stream (e.g., a company updates stream), via which messages or status updates published on behalf of the company may be presented.
  • a data feed or content stream e.g., a company updates stream
  • the content that is presented in the company updates stream may be a combination of content that has been automatically generated by some application or service of the social network service, and content that has been published by an administrator or representative of the company who has been granted the authority to publish content on behalf of the company.
  • the messages or status updates that may be published on behalf of the company may be visible to all members of the social network service via the company page 130, regardless of whether a member is following the particular company and regardless of whether the member possesses the particular member profile attributes selected as targeting criteria by the author of the content, when the message or status update was initially published.
  • status updates in the company updates stream may only be visible to those members of the social network service who are following the company and/or possess the member profile attributes selected as targeting criteria by the publisher of the status update.
  • the messages or status updates published on behalf of the company may only appear in a member's personal data feed or content stream if the member has subscribed to receive messages (e.g., if the member is following the company), and the member possesses the member profile attributes that have been selected as targeting criteria by the publisher of the status update when publishing the message.
  • the published messages or status updates may be prioritized in accordance with the prioritization techniques described more fully below. For example, a message comprising a job opportunity may be given a high priority if the recipient is a follower of the company and has indicated an interest in employment opportunities.
  • the author can select as targeting criteria whether a message or status update should be communicated to employees, non-employees, or both employees and non-employees of the company on whose behalf the message or status update is being published, as depicted in FIG. 5. For example, if an author of a message being published on behalf of Avocado would like the message to be received only by employees of the company, the author can select "Employees", thereby limiting the audience to only employees of the company.
  • FIG. 6 is a user interface diagram illustrating an example of a user interface 650 or web page having a personalized data feed (or content stream) via which a member of a social network service may receive communication messages and/or status updates 654, according to some embodiments.
  • a content module 652 may represent a personalized data feed or content stream for a member of the social network service with the name John Smith.
  • the content stream not only does the content stream present content selected specifically for John Smith, the content stream itself may be presented within a user interface or a web page that is personalized for John Smith.
  • a personalized data feed or content stream has various configuration settings associated with it that may enable the user to specifically filter or select the type of content the member desires to view in the personalized content stream.
  • high priority communications may be identified in the personalized data feed or content stream. For example, as noted above, a message 660 comprising a job opportunity may be given a high priority if the recipient has indicated an interest in employment opportunities.
  • a path score may be derived to reflect the overall connection strength (or relevance) of the path connecting the sender and the recipient.
  • the path score may be derived by simply aggregating (e.g., summing, or otherwise combining with an algorithm or formula) the individual edge scores that correspond with the edges 334 connecting the nodes 332, 336 that ultimately connect the sender and the recipient.
  • a variety of algorithms may be used to derive the individual edge scores for a particular edge 334 and/or edge type connecting any two nodes 332, 336 in the social graph.
  • various weighting factors may be applied to influence (e.g., increase or decrease) the edge score for a particular edge type (e.g., the type of association existing between two nodes 332, 336 in the social graph), based on the particular task for which the connection paths are being identified and presented.
  • a visual representation of the connection path having the highest path score may be presented to the user.
  • a visual representation of several independent connection paths may be presented.
  • the path score may be used to prioritize communications received by a recipient, as described more fully below.
  • the message sender when a message sender has addressed a message to another member of the social network service (e.g., a message recipient), the message sender may be presented with a visual representation of the best connection path or paths connecting the message sender to the message recipient, as determined by analysis of the social graph maintained by the social network service.
  • the algorithm used to derive the path scores for the various connection paths connecting the message sender to the message recipient may be selected based on an inferred type of communication, or an explicitly selected type of communication.
  • the social network service may use machine learning techniques and/or various algorithms to infer the type of communication (e.g., the purpose or reason the message sender is
  • the message sender may explicitly select or otherwise specify the type of communication, such that the selected communication type will influence the algorithm used to derive the path scores for the connection paths connecting the message sender with the message recipient.
  • the algorithm that is used to derive the path scores may be presented to the user, based on the task and context in which the task is being performed.
  • the visual representation of the best connection path or paths may be automatically embedded or otherwise included in the content of a message being prepared by the message sender.
  • the path score corresponding to the connection path or paths with the highest path scores is embedded or otherwise included in the content of a message. Consequently, when the message recipient receives the message, the message recipient may determine the best connection path or paths connecting the message sender with the message recipient and/or may view a visual
  • connection path or paths may be determined and/or may be presented in a manner that allows the message sender to simply reference the relevant information when the message sender is composing the message.
  • the visual representation of the connection path may be presented as a separate element of a graphical user interface displayed when the message sender is composing the message.
  • the visual representation of the connection path or paths may be presented to a message recipient, not as part of a received electronic message, but instead as part of a separate user interface element that is presented when the message recipient is accessing and viewing the electronic message.
  • the message recipient is more likely to be receptive to receiving, reading, and replying to the message, and the message is more easily prioritized. This is particularly beneficial in an environment where people are frequently overloaded with information and are receiving hundreds of messages per day.
  • the path score embedded or otherwise included in the content of a message may be utilized to prioritize a
  • a table 738 illustrates a non-exhaustive list of associations that may be attributed to an edge 334 connecting two nodes 332, 336, representing entities, in the social graph data structure.
  • Various edge types or associations may be applicable to all combinations of entity types, while others may be applicable to only a certain subset of combinations of entity types.
  • an edge type representing a "following" relationship may connect two nodes 332, 336, where each node 332, 336 represents a person, and the edge 334 connecting the two nodes 332, 336 may indicate that one person is following the other.
  • an edge 334 representing a "following" relationship may connect a first node 332, representing a person, with a second node 336, representing a company, to indicate that the person is following the company.
  • edge type for a "following" relationship may apply to the entity type "person” as well as “company.”
  • Some other associations may only be meaningful when applied to an edge 334 connecting certain types of entities.
  • Some of the various associations or edge types shown in FIG. 7 may indicate a particular relationship that exists between two entities represented by nodes 332, 336 in the graph data structure. For instance, two members of the social network service may be directly connected, one member may be following another, one member may be in an address book or contacts list of another, two members may be co-managing a group or co-inventors on a patent, and so forth.
  • the association or edge type may be assigned to the edge 334 connecting the two nodes 332, 336 representing the two entities (e.g., person, company, educational institution, group, etc.).
  • Some of the various associations or edge types shown in FIG. 7 may indicate an activity that is shared in common between two entities, or an activity that involves two entities. For example, a first member may have communicated a message to a second member. A first member may have re-tweeted or forwarded some content item (e.g., a tweet) that was originally generated by a second member. A first member may share an item of content with a second member or comment on an item of content posted by a second member, and so forth.
  • some content item e.g., a tweet
  • a third category of associations may generally involve what may be thought of as affiliations. For instance, a first member may be affiliated with a second member based on membership in the same group. Similarly, two members may be, or, have been, employed with the same company at different times, or simultaneously. Two members may be affiliated based on having attended the same school or university, and so on.
  • Another general category of associations or edge types involves what are referred to herein as affinities. For instance, two members may be associated based on an affinity or similarity of profile attributes, such as the same general geographic location, skills shared in common, employment in the same industry, common degrees or majors, and the like.
  • the various associations or edge types that may be assigned to an edge 334 connecting two nodes 332, 336 in a graph data structure 330 presented in FIG. 7 are simply some of the many examples.
  • different associations (not shown in FIG. 7) may also be used, particularly with embodiments of the invention that have additional entity types other than the specific examples presented herein (e.g., person, company, educational institution, group).
  • FIG. 8 is a diagram illustrating an example of a generalized social graph 840 containing nodes representing several different entities, having varying entity types including members, companies, and schools.
  • Member nodes include John Doe 842 and Jane Smith 844.
  • Company nodes include ACME Products Inc. 846, Widget Corp. 848, and XYZ Inc. 850.
  • the graph 840 may contain edges connecting nodes representing entities of either the same or different types. For example, there is an edge 854 connecting John Doe 842 with Jane Smith 844, reflecting the fact that John Doe 842 and Jane Smith 844 are directly connected to each other.
  • This edge 854 may be assigned an edge score or weight indicating the strength of the connection between John Doe 842 and Jane Smith 844.
  • the weight may be computed using a measure of the overlap between the member connections in John's network and Jane's network. That is, for two members Ml and M2,
  • W(M1, M2) Conn(Ml, M2) / SQRT[Conn(Ml) * Conn(M2)]
  • W(M1, M2) denotes the weight of the edge connecting Ml and M2
  • Conn(Ml, M2) may denote the number of direct member connections that Ml and M2 have in common
  • Conn(Ml) may denote the total number of direct member connections in Mi's network
  • Conn(M2) may denote the total number of direct member connections in M2's network.
  • the weight for this edge 854 may be determined based on a statistical estimate of the probability that John and Jane know each other, or by other algorithms or techniques or combinations thereof.
  • the score or weight assigned to this edge 856 may indicate the strength of this affiliation.
  • the weight may be computed based on the overlap between Jane's network and the network of ACME Products Inc. 846, where the node in the social graph 840 representing ACME Products Inc. 846 is connected to each member who is a current or former employee of the company.
  • W(M1, CI) Conn(Ml, CI) / SQRT[(Conn(Ml) * Conn(Cl)] where W(M1, CI) may denote the weight of the edge 334 connecting Ml and CI, Conn(Ml,
  • CI may denote the number of members Ml is connected to who are also current or past employees of CI
  • Conn(Ml) denotes the total number of connections in Mi's network
  • Conn(Cl) may denote the total number of members who are current or past employees of CI.
  • edge 858 connecting Jane Smith 844 to State University 852, which represents Jane's affiliation with State University 852 as an alumnus of the university. The weight of this edge 858 may indicate the strength of this affiliation.
  • Conn(Ml) may denote the total number of member connections in Mi 's network.
  • Widget Corp. 848 which represents the association between the two companies.
  • An association between two companies may exist for a variety of reasons (for example, if they share a common founder, if some members of the social network service have been employed at both companies, if one company is a subsidiary of the other, or if the two companies are business partners).
  • ACME Products Inc. 846 and Widget Corp. 848 are connected because a large number of former Widget Corp. 848 employees are currently employed with ACME Products Inc. 846.
  • the weight of the edge 860 may denote the strength of the association.
  • the weight assigned to the edge 862 may indicate the strength of the association.
  • the social graph 840 may be a directed graph.
  • each edge 334 connecting the nodes representing two members may be a directed link from the followed member to the following member.
  • the followed member may send messages to the following member, but the following member cannot send messages to the followed member.
  • a social network may contain bi-directional connections between members, but an edge 334 connecting two nodes 332, 336 may have different weights depending on the direction.
  • the chief executive officer (CEO) of a company could be connected to an engineer, with the CEO having greater influence on the engineer than vice versa.
  • the social graph 840 may be an undirected graph, in which all connections between entities are bidirectional, and each edge 334 in the graph has equal weight in both directions. Accordingly, with some embodiments, the weight assigned to a particular edge 334 may influence the measure of connection strength between two nodes 332, 336 in general, and a particular connection path specifically.
  • the pathfinder module may generate a path score for each connection path identified.
  • the path score for each individual connection path may be dependent upon the individual edge scores assigned to the edges 334 connecting the nodes 332, 336 in the connection path.
  • different algorithms may be automatically used to derive path scores for different applications or tasks. The particular algorithm used to derive the path score may be automatically and dynamically selected, for example, based on a determination of what the user is attempting to achieve.
  • the user may make an explicit selection (e.g., by specifying a type or category of message, or a purpose for contacting someone), which will then influence the algorithm used to derive the path scores. Consequently, the edge scores or weights for different edge types may be derived differently, for example, to increase or decrease the influence of edge scores of certain types of edges 334 on the path score, depending upon the particular application that has invoked the pathfinder module 216, or a particular task or process being undertaken or performed.
  • the pathfinder module 216 may be used with an email application, an instant messaging (IM) application, a text or SMS (short message service) text messaging application, or even certain telephone or voice communication systems to include any of a variety of voice over IP (VoIP) based services.
  • IM instant messaging
  • SMS short message service
  • VoIP voice over IP
  • the pathfinder module 216 may be implemented for use with applications that use any of a variety of network or computing models, to include web-based applications, client-server applications, or even peer-to-peer applications.
  • the messaging application may be a service that is integrated with the social network service, and thus hosted by the same entity that operates the social network service and the pathfinder service.
  • the pathfinder service may be accessible (e.g., via an API) to third-party applications that are hosted by entities other than the entity that operates the social network service.
  • FIG. 9 is an example of a user interface for use with a messaging application that implements a method, consistent with some embodiments of the invention.
  • a window pane 942 may include a text input box 944 for specifying the identity (e.g., name, email address, phone number, etc.) of a person to whom a message 920 is to be communicated.
  • various other user interface elements for inputting or providing information may be presented.
  • a drop down box 946 may enable the message sender to specify a category of message that is to be communicated to the message recipient.
  • a sender can mark a communication as urgent or specify an action (reference number 916).
  • the category of message selected by the message sender may influence the algorithm used to derive path scores for the different connection paths that connect the message sender to the message recipient.
  • the messaging application may present information about the message recipient, for example, as shown in a window pane 948.
  • the pathfinder module 216 may identify the strongest connection paths between the message sender and the message recipient, and present a visual representation of the strongest connection paths.
  • a window pane 950 may include a visual representation of the strongest connection paths - that is, the connection paths with the highest path scores - connecting the message sender, Ashley Hall 952, with the message recipient, John Wallace 954, via two mutual connections, Jane Doe and Kevin Smith.
  • the path score may be used to prioritize a communication.
  • a team- sharing application may enable a team to establish an environment in which to share information and provide for communications between team members.
  • the team- sharing environment may be oriented around team members and relationships.
  • only team members may have access to the team- sharing environment.
  • invited guests may also have access to the team-sharing environment and/or access to the environment may be unrestricted.
  • FIG. 10 is a representation of an example user interface for a team- sharing application, consistent with some embodiments of the invention.
  • a team content feed 1020 is generated to provide status updates, notifications, and/or alerts to team members.
  • a comment entry field 1008 may enable a user to enter a comment into the content feed 1020, or send a comment to a specific user or group of users.
  • comment 1010 may be entered into the team content feed 1020 via the comment entry field 1008.
  • a team interface 1030 may display one or more teams accessible by a user for team sharing.
  • a Research team may comprise members interested in research topics related to an organization or corporation.
  • a favorite discussions interface 1034 may provide access to one or more active discussions that may be of interest to the user.
  • the favorite discussions interface 1034 may display the latest comment(s) that have been added to each of the discussions.
  • One or more of the discussions may correspond to one of the teams identified in the team interface 1030.
  • a co-worker interface 1042 may display thumbnail pictures representing one or more co-workers of a user.
  • a thumbnail picture may be selected to retrieve the profile of the associated co-worker and/or to create a message 920 for the selected co-worker.
  • the team members may be inferred from a member profile, connections, and activities, and the importance of an information item shared with team members may be determined based on the experience, skills, relationships, and actions of one or more of the team members.
  • a team member may post the status of a work item, such as the status of a collaborative presentation, on the team content feed 1020.
  • an activity may automatically generate a posting to the team content feed 1020.
  • the uploading of a computer program to a database may automatically generate a posting to the team content feed 1020 indicating that the computer program is available for testing.
  • the posting may appear on the team content feed 1020 of all team members, or may appear on the team content feed 1020 of a subset of the team members.
  • a posting that a computer program is available for testing may only appear on the team content feed 1020 of team members who are also members of a software-testing department.
  • a team member may issue questions or requests via a team content feed 1020 to another team member, or to a plurality of team members.
  • processors may be 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 or objects that operate to perform one or more operations or functions.
  • the modules and objects referred to herein may, in some example embodiments, comprise processor-implemented modules and/or objects.
  • 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.
  • the one or more processors may also operate to support performance of the relevant operations in a "cloud computing" environment or within the context of "software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., APIs).
  • SaaS software as a service
  • FIG. 11 is a block diagram of a machine in the form of a computer system 1100 within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein.
  • the machine operates as a standalone device or may be connected (e.g., networked) to other machines.
  • 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 peer-to-peer (or distributed) network environment.
  • the machine will be a server computer.
  • the machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine.
  • PC personal computer
  • PDA personal digital assistant
  • STB set-top box
  • PDA personal digital assistant
  • mobile telephone a web appliance
  • network router switch or bridge
  • machine any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine.
  • 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 1100 includes a processor 1102 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 1101 and a static memory 1106, which communicate with each other via a bus 1108.
  • the computer system 1100 may further include a video display unit 1110, an alphanumeric input device 1117 (e.g., a keyboard), and a user interface (UI) navigation device 1111 (e.g., a mouse).
  • the video display unit 1110, input device 1117 and user interface navigation device 1111 are a touch screen display.
  • the computer system 1100 may additionally include a storage device (e.g., drive unit) 1116, a signal generation device 1118 (e.g., a speaker), a network interface device 1120, and one or more sensors 1121, such as a global positioning system sensor, compass,
  • the drive unit 1116 includes a machine-readable medium 1122 on which is stored one or more sets of data structures and instructions 1123 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein.
  • the instructions 1123 may also reside, completely or at least partially, within the main memory 1101 and/or within the processor 1102 during execution thereof by the computer system 1100, with the main memory 1101 and the processor 1102 also constituting machine-readable media 1122.
  • machine-readable medium 1122 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 1123.
  • the term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions 1123 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 1123.
  • the term “machine- readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media.
  • machine-readable media 1122 include non- volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically-erasable programmable read-only memory (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.
  • semiconductor memory devices e.g., erasable programmable read-only memory (EPROM), electrically-erasable programmable read-only memory (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 1123 may further be transmitted or received over a communications network 1126 using a transmission medium via the network interface device 1120 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).
  • POTS plain old telephone
  • wireless data networks e.g., Wi-Fi® and WiMax® networks.
  • transmission medium shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions 1123 for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Health & Medical Sciences (AREA)
  • Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne des procédés, des systèmes et des produits de programmes informatiques destinés à identifier une relation entre une pluralité d'entités. Des membres d'une entité sont segmentés en un ou plusieurs groupes d'après un ou plusieurs attributs; un ou plusieurs des groupes sont analysés pour déterminer une fonction du groupe correspondant. Une paire de groupes est sélectionnée, chaque groupe de la paire de groupes étant issu d'une entité différente. Une relation entre la paire de groupes sélectionnée est analysée pour générer une métrique de relation, et la relation entre la paire de groupes sélectionnée est caractérisée d'après la métrique de relation.
PCT/US2015/023332 2014-06-30 2015-03-30 Détermination d'un type de relation entre entités disparates WO2016003512A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201462018797P 2014-06-30 2014-06-30
US62/018,797 2014-06-30

Publications (1)

Publication Number Publication Date
WO2016003512A1 true WO2016003512A1 (fr) 2016-01-07

Family

ID=55019820

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2015/023332 WO2016003512A1 (fr) 2014-06-30 2015-03-30 Détermination d'un type de relation entre entités disparates

Country Status (2)

Country Link
CN (1) CN105279597B (fr)
WO (1) WO2016003512A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018030676A1 (fr) * 2016-08-08 2018-02-15 건국대학교 산학협력단 Procédé et dispositif de prévention de l'errance enfantine
US10044775B2 (en) 2014-08-29 2018-08-07 Microsoft Technology Licensing, Llc Calculating an entity'S location size via social graph
US10074143B2 (en) 2014-08-29 2018-09-11 Microsoft Technology Licensing, Llc Surfacing an entity's physical locations via social graph
US10523736B2 (en) 2014-06-30 2019-12-31 Microsoft Technology Licensing, Llc Determining an entity's hierarchical relationship via a social graph
US20200019902A1 (en) * 2017-03-28 2020-01-16 Yokogawa Electric Corporation Engineering assistant system, engineering assistant method, server apparatus, storage medium, client apparatus, and client program

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106131296B (zh) * 2016-06-07 2019-06-11 腾讯科技（深圳）有限公司 信息展示方法及装置
JP6129393B1 (ja) * 2016-09-09 2017-05-17 株式会社あしたのチーム コンピテンシー目標設定支援装置，支援方法，及び支援プログラム

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110093420A1 (en) * 2009-10-16 2011-04-21 Erik Rothenberg Computer-processing system scoring subjects relative to political, economic, social, technological, legal and environmental (pestle) factors, utilizing input data and a collaboration process, transforming a measurement valuation system regarding the value of subjects against an agenda
WO2012178130A2 (fr) * 2011-06-24 2012-12-27 Monster Worldwide, Inc. Appareils, procédés et systèmes pour plateforme de correspondance sur le plan social
US20130318180A1 (en) * 2012-05-25 2013-11-28 Mohammad Shafkat Amin Leveraging a social graph to deliver relevant recommendations
US20140075004A1 (en) * 2012-08-29 2014-03-13 Dennis A. Van Dusen System And Method For Fuzzy Concept Mapping, Voting Ontology Crowd Sourcing, And Technology Prediction
WO2014074607A2 (fr) * 2012-11-08 2014-05-15 Linkedin Corporation Approbations de compétences

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101436191A (zh) * 2007-11-13 2009-05-20 日电（中国）有限公司 用于计算对象之间竞争性度量的方法与系统
CN103827895B (zh) * 2011-02-22 2017-08-08 汤姆森路透社全球资源公司 实体指纹
WO2013030830A1 (fr) * 2011-08-31 2013-03-07 Daniel Levy Classement automatique d'entités en fonction d'interactions entre elles
CN103428164B (zh) * 2012-05-15 2015-07-01 腾讯科技（深圳）有限公司 用户社交网络关系圈划分方法和系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110093420A1 (en) * 2009-10-16 2011-04-21 Erik Rothenberg Computer-processing system scoring subjects relative to political, economic, social, technological, legal and environmental (pestle) factors, utilizing input data and a collaboration process, transforming a measurement valuation system regarding the value of subjects against an agenda
WO2012178130A2 (fr) * 2011-06-24 2012-12-27 Monster Worldwide, Inc. Appareils, procédés et systèmes pour plateforme de correspondance sur le plan social
US20130318180A1 (en) * 2012-05-25 2013-11-28 Mohammad Shafkat Amin Leveraging a social graph to deliver relevant recommendations
US20140075004A1 (en) * 2012-08-29 2014-03-13 Dennis A. Van Dusen System And Method For Fuzzy Concept Mapping, Voting Ontology Crowd Sourcing, And Technology Prediction
WO2014074607A2 (fr) * 2012-11-08 2014-05-15 Linkedin Corporation Approbations de compétences

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10523736B2 (en) 2014-06-30 2019-12-31 Microsoft Technology Licensing, Llc Determining an entity's hierarchical relationship via a social graph
US10044775B2 (en) 2014-08-29 2018-08-07 Microsoft Technology Licensing, Llc Calculating an entity'S location size via social graph
US10074143B2 (en) 2014-08-29 2018-09-11 Microsoft Technology Licensing, Llc Surfacing an entity's physical locations via social graph
WO2018030676A1 (fr) * 2016-08-08 2018-02-15 건국대학교 산학협력단 Procédé et dispositif de prévention de l'errance enfantine
KR20180016768A (ko) * 2016-08-08 2018-02-20 건국대학교 산학협력단 미아를 방지하기 위한 방법 및 장치
KR101866649B1 (ko) * 2016-08-08 2018-06-11 건국대학교 산학협력단 미아를 방지하기 위한 방법 및 장치
US20200019902A1 (en) * 2017-03-28 2020-01-16 Yokogawa Electric Corporation Engineering assistant system, engineering assistant method, server apparatus, storage medium, client apparatus, and client program
US11562309B2 (en) * 2017-03-28 2023-01-24 Yokogawa Electric Corporation System, method, and non-transitory computer readable medium for process engineering assistance

Also Published As

Publication number Publication date
CN105279597A (zh) 2016-01-27
CN105279597B (zh) 2020-11-24

Similar Documents

Publication Publication Date Title
US10523736B2 (en) Determining an entity's hierarchical relationship via a social graph
US20150379445A1 (en) Determining a relationship type between disparate entities
EP2673718B1 (fr) Exploitation d'un graphique social destiné à être utilisé avec une messagerie électronique
US10592535B2 (en) Data flow based feature vector clustering
US10311365B2 (en) Methods and systems for recommending a context based on content interaction
US20150379535A1 (en) Determining a function of an entity
US20130254213A1 (en) Techniques for identifying and presenting connection paths
CN105279597B (zh) 确定全异实体之间的关系类型
US9628430B2 (en) Notifications based on social network service activity and profile triggers
US10831846B2 (en) Adjusting search results based on overlapping work histories
US10216749B2 (en) Performing actions associated with positive feedback events
US9009249B2 (en) Systems and methods for delivering content to a mobile device based on geo-location
US9756006B2 (en) Contact prioritization and assignment using a social network
US20140244612A1 (en) Techniques for quantifying the intent and interests of members of a social networking service
US20150006242A1 (en) Techniques for quantifying the intent and interests of members of a social networking service
US20150242967A1 (en) Generating member profile recommendations based on community overlap data in a social graph
US20140214960A1 (en) Methods and systems for targeting query messages in a social graph
US20140143166A1 (en) Identifying members of a social network as candidate referral sources
US20140067964A1 (en) Targeted status updates
US20140244775A1 (en) Prioritization of network communications
US20140244561A1 (en) Providing recommendations to members of a social network
US20140245184A1 (en) Presenting actionable recommendations to members of a social network
US20170004453A1 (en) Job type recommendation engine
US20140149219A1 (en) Systems and methods for delivering content to a mobile device based on geo-location
US20160321761A1 (en) Analytics for the presentation of gems

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15814710

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15814710

Country of ref document: EP

Kind code of ref document: A1