Abstract: An influential persons identification system and method for identifying a set of influential persons (or influencers) in a social network (such as an online social network). The influential persons set is generated such that by sending a message to the set the message will be propagated through the network at the greatest speed and coverage. A ranking of users is generated, and a pruning process is performed starting with the top-ranked user and working down the list. For each user on the list, the user is identified as an influencer and then the user and each of his friends are deleted from the social network users list. Next, the same process is performed for the second-ranked user, the third-ranked user, and so forth. The process terminates when the list of users of the social network is exhausted or the desired number of influencers on the influential person set is reached.

Abstract: Methods and computer storage media for clustering nodes are provided. An input file is received that is comprised of primary nodes, secondary nodes and metrics that relate to the association between the primary nodes and the secondary nodes. Upon receiving the input file, the input file is abridged to reduce the number of nodes contained in the input file. The unique initial primary nodes are then clustered with their associated secondary node. The clusters containing the unique initial primary nodes are replaced if a subsequent related cluster satisfies a pre-defined condition. In some embodiments, multiple clusters are then merged until the cluster size reaches a pre-defined size. In some embodiments, the input file is cleaned and sorted prior to being abridged.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that utilize relevance judgments to estimate a discounted cumulative gain. In embodiments, a query:ad pair including a search query and at least one advertisement is identified. The at least one advertisement is identified as having not been previously associated with the search query and not as not having associated therewith a relevance judgment. An estimated relevance judgment is calculated and utilized to calculate an estimated discounted cumulative gain for the search query.

Abstract: Methods and computer storage media for clustering nodes are provided. An input file is received that is comprised of primary nodes, secondary nodes and metrics that relate to the association between the primary nodes and the secondary nodes. Upon receiving the input file, the input file may be abridged to reduce the number of nodes contained in the input file. The unique initial primary nodes may then be clustered with their associated secondary node. The clusters containing the unique initial primary nodes may then be replaced if a subsequent related cluster satisfies a pre-defined condition. In some embodiments, multiple clusters may then be merged until the cluster size reaches a pre-defined size. In some embodiments, the input file is may be cleaned and sorted prior to being abridged.

Abstract: Methods and computer storage media for clustering nodes are provided. An input file is received that is comprised of primary nodes, secondary nodes and metrics that relate to the association between the primary nodes and the secondary nodes. Upon receiving the input file, the input file is abridged to reduce the number of nodes contained in the input file. The unique initial primary nodes are then clustered with their associated secondary node. The clusters containing the unique initial primary nodes are replaced if a subsequent related cluster satisfies a pre-defined condition. In some embodiments, multiple clusters are then merged until the cluster size reaches a pre-defined size. In some embodiments, the input file is cleaned and sorted prior to being abridged.

Abstract: Methods and computer storage media for clustering nodes are provided. An input file is received that is comprised of primary nodes, secondary nodes and metrics that relate to the association between the primary nodes and the secondary nodes. Upon receiving the input file, the input file is abridged to reduce the number of nodes contained in the input file. The unique initial primary nodes are then clustered with their associated secondary node. The clusters containing the unique initial primary nodes are replaced if a subsequent related cluster satisfies a pre-defined condition. In some embodiments, multiple clusters are then merged until the cluster size reaches a pre-defined size. In some embodiments, the input file is cleaned and sorted prior to being abridged.

Abstract: A method and system are provided for identifying groups in large-scale networks. The large-scale networks include a collection of nodes and edges that may represent relationships between entities or individuals. The large-scale network is split into a number of fractions satisfying an edge threshold. In turn, the nodes in each fraction are merged to generate one or more clusters based on a specified similarity metric. The large-scale network is recursively split and clustered until distinct groups are identified.

Abstract: Methods and computer storage media for clustering nodes are provided. An input file is received that is comprised of primary nodes, secondary nodes and metrics that relate to the association between the primary nodes and the secondary nodes. Upon receiving the input file, the input file is abridged to reduce the number of nodes contained in the input file. The unique initial primary nodes are then clustered with their associated secondary node. The clusters containing the unique initial primary nodes are replaced if a subsequent related cluster satisfies a pre-defined condition. In some embodiments, multiple clusters are then merged until the cluster size reaches a pre-defined size. In some embodiments, the input file is cleaned and sorted prior to being abridged.

Abstract: A method and system are provided for identifying groups in large-scale networks. The large-scale networks include a collection of nodes and edges that may represent relationships between entities or individuals. The large-scale network is split into a number of fractions satisfying an edge threshold. In turn, the nodes in each fraction are merged to generate one or more clusters based on a specified similarity metric. The large-scale network is recursively split and clustered until distinct groups are identified.

Abstract: A system, method, computer-readable media, and related techniques are disclosed for predicting demographic information of a user. A social link network is created and a search request for demographic information related to a first user within the social link network is received. The requested demographic information based on the demographic information of other users connected to the first user within the social link network is provided.

Abstract: A social network visualization and mining system that includes a visualization application for mining social networks of users in an online social network. This visualization can be used to mine the social network for additional information and intelligence. The social network is displaying in graphical form, such as a node-link graph, with a center node representing the social network of a user being examined, and secondary nodes represent the primary user's friends. Lines represent links between the primary user and his friends, while various visualization features such as line thickness, line color, and text size are used to easily identify the type of relationship between users. The system also includes a topics visualization module, which builds and displays a social network based on a certain topic or keyword that is entered by the application user. A demographic prediction module examines a user's social network to predict demographics of users.

Abstract: An influential persons identification system and method for identifying a set of influential persons (or influencers) in a social network (such as an online social network). The influential persons set is generated such that by sending a message to the set the message will be propagated through the network at the greatest speed and coverage. A ranking of users is generated, and a pruning process is performed starting with the top-ranked user and working down the list. For each user on the list, the user is identified as an influencer and then the user and each of his friends are deleted from the social network users list. Next, the same process is performed for the second-ranked user, the third-ranked user, and so forth. The process terminates when the list of users of the social network is exhausted or the desired number of influencers on the influential person set is reached.