Abstract:
Equivalent concepts expressed across multiple domains are matched and associated with a metapage generated by a social networking system. User preferences expressed on multiple domains, represented as pages in a social networking system, may be organized by concept and shared with advertisers, third-party developers, and other users of the social networking system using the metapages generated for the concepts. Aggregated social information may be presented to users of the social networking system viewing a page associated with a metapage. Information presented on external websites may be used to link pages across multiple domains with a metapage generated on the social networking system. In one embodiment, the information on other external websites associated with the metapage may be presented as links on the pages associated with the metapage. Feedback from users may be used to include or exclude pages from being associated with a generated metapage. In one embodiment, a best page may be determined for a concept embodied in multiple pages on the social networking system using a hierarchy of rules. In another embodiment, a best page may be determined for a user based on information about the user. In yet another embodiment, social context information may be provided on a page associated with a metapage for a viewing user that shows expressions of interest by other users on the page and other pages associated with the metapage.
Abstract:
At least one embodiment of this disclosure includes a method of inferring attribute labels for a user in a social networking system based on the user's social connections and user-specified attribute labels in the social networking system. The method can include: establishing variational equations based on attribute labels of nodes in an ego network in a social graph of a social networking system; determining likelihood scores for at least a portion of the attribute labels of neighboring nodes from a focal user node in the ego network based on user-specified attribute labels from the social networking system; and calculating probability distributions of possible attribute labels for the focal user node of the ego network based on the variational equations and the likelihood scores.
Abstract:
Embodiments are disclosed for striping a directed graph, e.g., a social graph, so as to efficiently perform an operation to each node in the directed graph. At least some of the embodiments can select first and second sets of nodes from the directed graph to form first and second stripes. The first and second sets of nodes are selected, for example, based on available computing resources. First and second intermediate results can be generated by performing the operation to each node of the first and the second stripes, respectively. The operation iteratively performs a superstep. The first and the second intermediate results are combined to form a collective result as an output of the superstep.
Abstract:
In one embodiment, a computing device accesses a social graph comprising nodes and edges connecting the nodes. Each of the edges between two of the nodes represents a single degree of separation between them. The nodes include user nodes corresponding to users of an online social network, and concept nodes corresponding to places. A particular place corresponds to a particular concept node, and corresponds to an associated location and a perimeter. A number of check-ins are received, each check-in being associated with the particular place and having a geographic location. Each check-in corresponds to an edge of the social graph between a user node and the particular concept node. Based on the received check-ins the computing device determines whether to update the associated location and the perimeter.
Abstract:
At least one embodiment of this disclosure includes a method of inferring attribute labels for a user in a social networking system based on the user's social connections and user-specified attribute labels in the social networking system. The method can include: establishing variational equations based on attribute labels of nodes in an ego network in a social graph of a social networking system; determining likelihood scores for at least a portion of the attribute labels of neighboring nodes from a focal user node in the ego network based on user-specified attribute labels from the social networking system; and calculating probability distributions of possible attribute labels for the focal user node of the ego network based on the variational equations and the likelihood scores.