Abstract: Method and system to determine a company rank utilizing on-line social network data are described. A company ranking system is configured to access a base set of member profiles, construct a talent flow graph having nodes that represent respective companies and edges that represent transitions of employees from one company to another, and determine a node score for each node by applying a ranking algorithm to the graph. In one embodiment, a company ranking system generates perturbed versions of the base set by using bootstrap resampling procedure and uses the perturbed versions of the base set to calculate ranking data for the nodes in the talent flow graph in the form of node scores. The distribution of the node scores included in the ranking data calculated for a given node is used to determine a desirability score for the company represented by the node.

Abstract: Systems and methods for presenting recommendations for resources to be used by members in learning about and/or acquiring a skill are described. In some example embodiments, the systems and methods receive information associated with a skill from a member of a social network, identify members of the social network that are associated with the skill, receive information from the identified members of the social network that identifies one or more resources associated with the skill, and generate a list of recommended resources that is based on the information received from the identified members of the social network.

Abstract: Techniques for managing information describing a hierarchy of relationships between educational institutions are described. According to various embodiments, first feature data describing a first school and second feature data describing a second school is accessed via one or more databases. A confidence score is then generated based on a machine learned model, the first feature data and the second feature data, the confidence score indicating a probability that the second school is a sub-school of the first school. Thereafter, based on a comparison of the confidence score to a threshold, is it is determined that the second school is a sub-school of the first school. Hierarchy information identifying a hierarchy of relationships between a plurality of schools is then generated or modified, the hierarchy information indicating that the second school is a sub-school of the first school.

Abstract: Method and system to determine a company rank utilizing on-line social network data are described. A company ranking system is configured to access a base set of member profiles, construct a talent flow graph having nodes that represent respective companies and edges that represent transitions of employees from one company to another, and determine a node score for each node by applying a ranking algorithm to the graph. In one embodiment, a company ranking system generates perturbed versions of the base set by using bootstrap resampling procedure and uses the perturbed versions of the base set to calculate ranking data for the nodes in the talent flow graph in the form of node scores. The distribution of the node scores included in the ranking data calculated for a given node is used to determine a desirability score for the company represented by the node.

Abstract: A school ranking system may be configured to determine a rank of a school based on career outcomes data, which may be obtained from member profile data stored by an on-line social network system. Schools may be ranked on the basis of proportions of their graduates who obtained employment at some of the most desirable companies for a given profession or occupation. In order to make university rankings robust to potential noise in company desirability, a large number of perturbed sets of desirable companies are generated by repeatedly substituting a subset of companies from the set of desirable companies with companies outside that set.

Abstract: A school ranking system may be configured to determine a rank of a school based on career outcomes data. Career outcomes data is obtained, at least in part, from member profile data stored by an on-line social network system. The school ranking system uses a list of the top-ranked companies for generating ranking data and also determines how many companies are to be included in the list of the top-ranked companies.

Abstract: Method and system to determine a school rank utilizing on-line social network data is described. A school ranking system may be configured to determine a ranking of a school based on career outcomes data which may be Obtained from member profile data stored by an on-line social network system. The school ranking system may examine the member profiles and determine how many of the target school alumni can be considered successful alumni and then calculate a success score for a school as a number of successful alumni divided by the total number of the school's alumni.

Abstract: Method and system to determine a company rank utilizing on-line social network data is described. A company ranking system may examine member profiles representing respective members of an on-line social network system and extract transition data. From the transition data, the company ranking system may construct a company transition graph having nodes that represent respective companies and edges that represent transitions of employees from one company to another. A rank or a node score for each node of the company transition graph may be determined by applying a ranking algorithm to the graph. The scores generated for respective nodes in the company transition graph may be stored for future use in a database.

Abstract: A machine may be configured to identify skills for a projected next job position. For example, the machine performs an analysis of profile data associated with a plurality of members of a social network service. Based on the analysis of the profile data and a present job position of a particular member of the social network service, the machine identifies a projected next position in a career path model. The machine identifies a cohort of other members of the social network service. The identifying of the cohort may be based on each cohort member holding the projected next position in a respective career. Each cohort member may be associated with one or more skills. The machine identifies one or more over-indexed skills for the cohort based on the one or more skills of the cohort members. The machine generates a recommendation that includes the one or more over-indexed skills.

Abstract: A method can include receiving a request for content including an attribute for a school. The method can analyze a social graph to identify a first set of nodes representing members, which can connect to a first node representing a first school, the social graph can be represented by a graph data structure having nodes representing various entities and edges representing associations between entities. The method can analyze a social graph to identify a second set of nodes representing members, the second set of nodes representing members connecting to a second node representing a school. The method can generate a central tendency of a number of the first set of nodes representing members and a number of the second set of nodes representing members and using the central tendency and a number of the first set of nodes representing members, can determine an index number for the first school.

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums which automatically convert an unstandardized attribute value of a member profile of a social networking service to one of a plurality of standardized values for that attribute. In some examples, the method utilizes various matching and similarity metrics in combination with social aspects available to a social networking service to determine the best standardized value that matches the unstandardized value entered by the user.

Abstract: Systems and methods for presenting recommendations for resources to be used by members in learning about and/or acquiring a skill are described. In some example embodiments, the systems and methods receive information associated with a skill from a member of a social network, identify members of the social network that are associated with the skill, receive information from the identified members of the social network that identifies one or more resources associated with the skill, and generate a list of recommended resources that is based on the information received from the identified members of the social network.

Abstract: Systems and methods for providing career recommendations to a member of a social network are described. In some example embodiments, the systems and methods receive input associated with a professional or aspirational goal from a member of a social network, determine a recommendation based on information stored by the social network, and provide the recommendation to the member of the social network, among other things.

Abstract: Systems and methods for providing career recommendations to a member of a social network are described. In some example embodiments, the systems and methods receive input associated with a professional or aspirational goal from a member of a social network, determine a recommendation based on information stored by the social network, and provide the recommendation to the member of the social network, among other things.