Abstract: A social graph may be modeled as a collection of claims. Each claim is associated with an author, an audience, and an assertion about a fact. Probabilistic information may be collected from various sources for a claim, enabling a social networking system to evaluate a truthfulness of the assertion made in the claim. User-declared profile information may be evaluated as claims. A user, entity, or application may make claims about any assertions made in the social networking system. Reputation scores may be determined for users based on evaluations of their historical assertions. Claims may be evaluated for truthfulness using a probabilistic prediction model using heuristics analysis, regression analysis, and machine learning methods. A claims-based profile of users may be provided to viewers based on the contexts in which the claims were made. Viewers may view claims made about users, such as the users' biographical information, contact information, expertise, and interests.

Abstract: A social networking system receives contact information from a social networking system user. The received contact information is stored and associated with a user profile in the social networking system including information matching at least a portion of the stored content information. This increases the information associated with the user profile. Subsequently received content information is compared to user profiles and stored contact information associated with one or more user profiles. User profiles including information matching at least a portion of the subsequently received content information or associated with stored contact information matching at least a portion of the subsequently received content information are identified as potential connections for the user providing the subsequently received contact information.

Abstract: A social networking system receives contact information from a social networking system user. The received contact information is stored and associated with a user profile in the social networking system including information matching at least a portion of the stored content information. This increases the information associated with the user profile. Subsequently received content information is compared to user profiles and stored contact information associated with one or more user profiles. User profiles including information matching at least a portion of the subsequently received content information or associated with stored contact information matching at least a portion of the subsequently received content information are identified as potential connections for the user providing the subsequently received contact information.

Abstract: When a request to connected a requesting user to a target user is received by the social networking system, information associated with the requesting user and with users connected to the target user is retrieved. A fraud probability score indicating a probability that the requesting user is impersonating a user connected to the target user is determined based on the information associated with the requesting user and with users connected to the target user. Based on the fraud probability score, a determination is made whether the requesting user is a suspected imposter and remedial action is taken if imposter is suspected.

Abstract: When a request to connected a requesting user to a target user is received by the social networking system, information associated with the requesting user and with users connected to the target user is retrieved. A fraud probability score indicating a probability that the requesting user is impersonating a user connected to the target user is determined based on the information associated with the requesting user and with users connected to the target user. Based on the fraud probability score, a determination is made whether the requesting user is a suspected imposter and remedial action is taken if imposter is suspected.

Abstract: A social networking system receives contact information from a social networking system user. The received contact information is stored and associated with a user profile in the social networking system including information matching at least a portion of the stored content information. This increases the information associated with the user profile. Subsequently received content information is compared to user profiles and stored contact information associated with one or more user profiles. User profiles including information matching at least a portion of the subsequently received content information or associated with stored contact information matching at least a portion of the subsequently received content information are identified as potential connections for the user providing the subsequently received contact information.

Abstract: A social graph may be modeled as a collection of claims. Each claim is associated with an author, an audience, and an assertion about a fact. Probabilistic information may be collected from various sources for a claim, enabling a social networking system to evaluate a truthfulness of the assertion made in the claim. User-declared profile information may be evaluated as claims. A user, entity, or application may make claims about any assertions made in the social networking system. Reputation scores may be determined for users based on evaluations of their historical assertions. Claims may be evaluated for truthfulness using a probabilistic prediction model using heuristics analysis, regression analysis, and machine learning methods. A claims-based profile of users may be provided to viewers based on the contexts in which the claims were made. Viewers may view claims made about users, such as the users' biographical information, contact information, expertise, and interests.

Abstract: A social graph may be modeled as a collection of claims. Each claim is associated with an author, an audience, and an assertion about a fact. Probabilistic information may be collected from various sources for a claim, enabling a social networking system to evaluate a truthfulness of the assertion made in the claim. User-declared profile information may be evaluated as claims. A user, entity, or application may make claims about any assertions made in the social networking system. Reputation scores may be determined for users based on evaluations of their historical assertions. Claims may be evaluated for truthfulness using a probabilistic prediction model using heuristics analysis, regression analysis, and machine learning methods. A claims-based profile of users may be provided to viewers based on the contexts in which the claims were made. Viewers may view claims made about users, such as the users' biographical information, contact information, expertise, and interests.