Abstract: Techniques for detecting social influence between users in a set of users with regard to an activity are provided. Data for each user of the set of users is received that includes a time value at which the user became active with regard to the activity, and includes at least one indication of another user in the set of users associated with the user. A first estimate of social correlation in the set of users is determined based on the data. The data is modified. For instance, the data may be modified according to a shuffle test and/or an edge reversal test. A second estimate of social correlation in the set of users is determined based on the modified data. The first estimate is compared to the second estimate to determine a degree of social influence in the set of users.

Abstract: Techniques for detecting social influence between users in a set of users with regard to an activity are provided. Data for each user of the set of users is received that includes a time value at which the user became active with regard to the activity, and includes at least one indication of another user in the set of users associated with the user. A first estimate of social correlation in the set of users is determined based on the data. The data is modified. For instance, the data may be modified according to a shuffle test and/or an edge reversal test. A second estimate of social correlation in the set of users is determined based on the modified data. The first estimate is compared to the second estimate to determine a degree of social influence in the set of users.

Abstract: Embodiments of methods, apparatuses, devices and systems associated with sorting candidate values are disclosed.

Abstract: A social network is managed by applying connectivity and similarity measures to social network information to identify possible new relationships between social network users, and then automatically suggest those identified relationships to the social network users. The social network information can include user profile information and indicate existing social relationships between the users in the social network. Users can provide feedback regarding the suggestions, including indications whether the relationship was accepted, consummated, or declined. The social network information can be updated using the feedback. Similarity measures can be based on one or more of shared contacts, or common interests or activities, or content associated with social network users, or ratings within the social network of users and/or their content. Possible relationships having similarity measures that suggest the users likely to already know each other, can be omitted and not suggested.

Abstract: A system and method to facilitate real-time matching of content to advertising information in a network are described. A request for advertising information is received over a network, the advertising information to be displayed for a user entity in association with content information within a web page requested by the user entity, the request containing the content information, a web page identifier, and additional data associated with the web page. The content information is further analyzed in real-time to construct a page summary of the web page. The web page identifier and the additional data are further analyzed in real-time to extract at least one keyword relevant to the content information. Finally, the advertising information is determined in real-time based on the page summary and the extracted keywords.