Abstract: An online system generates implied links between objects distributed by the online system based on a plurality of users' behaviors with respect to the objects. For a behavior of a user with respect to an object that satisfies a link criterion, the online system identifies a connection between the user and the object. Local object graphs are generated to represent the relationships between the objects connected to each user, and the local object graphs are merged into a global object graph. An adjacency matrix is extracted from the global object graph as a representation of the strength of relationships between the objects distributed by the online system. The online system may then apply graph analysis techniques to the adjacency matrix, thereby analyzing the relationships between the objects without relying upon explicit links between the objects.

Abstract: A digital magazine server identifies content items for recommendation to a user based on content items with which the user previously interacted. Based on key phrases and terms in content items with which the user previously interacted, topics are associated with the content items and used to generate a vector for each content item. The vectors are used to generate clusters including one or more content items. A characteristic vector is generated for each cluster based on the vectors generated for content items within a cluster. Candidate content items are retrieved and topics included in the candidate content items are used along with the characteristic vectors to determine a measure of similarity between candidate content items and various clusters. Candidate content items with at least a threshold measure of similarity to a cluster are selected for presentation to the user.

Abstract: An online system generates implied links between objects distributed by the online system based on a plurality of users' behaviors with respect to the objects. For a behavior of a user with respect to an object that satisfies a link criterion, the online system identifies a connection between the user and the object. Local object graphs are generated to represent the relationships between the objects connected to each user, and the local object graphs are merged into a global object graph. An adjacency matrix is extracted from the global object graph as a representation of the strength of relationships between the objects distributed by the online system. The online system may then apply graph analysis techniques to the adjacency matrix, thereby analyzing the relationships between the objects without relying upon explicit links between the objects.

Abstract: An online system generates implied links between objects distributed by the online system based on a plurality of users' behaviors with respect to the objects. For a behavior of a user with respect to an object that satisfies a link criterion, the online system identifies a connection between the user and the object. Local object graphs are generated to represent the relationships between the objects connected to each user, and the local object graphs are merged into a global object graph. An adjacency matrix is extracted from the global object graph as a representation of the strength of relationships between the objects distributed by the online system. The online system may then apply graph analysis techniques to the adjacency matrix, thereby analyzing the relationships between the objects without relying upon explicit links between the objects.

Abstract: An online system generates implied links between objects distributed by the online system based on a plurality of users' behaviors with respect to the objects. For a behavior of a user with respect to an object that satisfies a link criterion, the online system identifies a connection between the user and the object. Local object graphs are generated to represent the relationships between the objects connected to each user, and the local object graphs are merged into a global object graph. An adjacency matrix is extracted from the global object graph as a representation of the strength of relationships between the objects distributed by the online system. The online system may then apply graph analysis techniques to the adjacency matrix, thereby analyzing the relationships between the objects without relying upon explicit links between the objects.

Abstract: A digital magazine server identifies content items for recommendation to a user based on content items with which the user previously interacted. Based on key phrases and terms in content items with which the user previously interacted, topics are associated with the content items and used to generate a vector for each content item. The vectors are used to generate clusters including one or more content items. A characteristic vector is generated for each cluster based on the vectors generated for content items within a cluster. Candidate content items are retrieved and topics included in the candidate content items are used along with the characteristic vectors to determine a measure of similarity between candidate content items and various clusters. Candidate content items with at least a threshold measure of similarity to a cluster are selected for presentation to the user.

Abstract: A digital magazine server creates cover pages identifying relevant content items relevant to a user. Candidate feeds of content items are identified from various sources, such as user-defined sections of content items, social networking systems, external content providers, and recommended content. The digital magazine server retrieves content items from the candidate feeds and generates clusters including retrieved content items based at least in part on the content elements contained in the content items. The content items in various clusters are scored, and one or more content items are selected from each cluster. The selected content items are placed in a consolidated feed, which is used to create a cover page describing a digital magazine for presentation to a digital magazine server user.

Abstract: An online system generates implied links between objects distributed by the online system based on a plurality of users' behaviors with respect to the objects. For a behavior of a user with respect to an object that satisfies a link criterion, the online system identifies a connection between the user and the object. Local object graphs are generated to represent the relationships between the objects connected to each user, and the local object graphs are merged into a global object graph. An adjacency matrix is extracted from the global object graph as a representation of the strength of relationships between the objects distributed by the online system. The online system may then apply graph analysis techniques to the adjacency matrix, thereby analyzing the relationships between the objects without relying upon explicit links between the objects.

Abstract: An online system generates implied links between objects distributed by the online system based on a plurality of users' behaviors with respect to the objects. For a behavior of a user with respect to an object that satisfies a link criterion, the online system identifies a connection between the user and the object. Local object graphs are generated to represent the relationships between the objects connected to each user, and the local object graphs are merged into a global object graph. An adjacency matrix is extracted from the global object graph as a representation of the strength of relationships between the objects distributed by the online system. The online system may then apply graph analysis techniques to the adjacency matrix, thereby analyzing the relationships between the objects without relying upon explicit links between the objects.