Label inference in a social network
    11.
    发明授权
    Label inference in a social network 有权
    社交网络中的标签推断

    公开(公告)号:US09552613B2

    公开(公告)日:2017-01-24

    申请号:US14272176

    申请日:2014-05-07

    Applicant: Facebook, Inc.

    CPC classification number: G06N5/04 G06F17/30867 G06N7/005 G06Q50/01

    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 translation: 本公开的至少一个实施例包括一种在社交网络系统中基于用户的社交连接和用户指定的社交网络系统中的属性标签推断用户的属性标签的方法。 该方法可以包括:基于社交网络系统的社交图中的自我网络中的节点的属性标签来建立变分方程; 基于来自所述社交网络系统的用户指定的属性标签,确定所述自我网络中的焦点用户节点的至少一部分相邻节点的属性标签的可能性得分; 并且基于变分方程和似然分数来计算自我网络的焦点用户节点的可能属性标签的概率分布。

    Striping of directed graphs and nodes with improved functionality
    13.
    发明授权
    Striping of directed graphs and nodes with improved functionality 有权
    引导图形和节点具有改进的功能

    公开(公告)号:US09330199B2

    公开(公告)日:2016-05-03

    申请号:US14336363

    申请日:2014-07-21

    Applicant: Facebook, Inc.

    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 translation: 公开了用于条带化有向图(例如,社交图)的实施例,以有效地对有向图中的每个节点执行操作。 至少一些实施例可以从有向图中选择第一和第二组节点以形成第一和第二条带。 例如,基于可用的计算资源来选择第一和第二组节点。 可以通过分别对第一和第二条纹的每个节点执行操作来生成第一和第二中间结果。 该操作迭代地执行一个超级步骤。 第一和第二中间结果被组合以形成作为超级步骤的输出的集合结果。

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