Abstract: The present invention relates to a system for graph visualization. The system includes a display and one or more processors with a memory. The processors perform several operations, including receiving as an input a graph G=(V,E), where V is a set of vertices and E is a set of edges between the vertices. The vertices are then randomly distributed through a unit square. The vertices are then arranged to generate an aesthetically pleasing graph, which is displayed on the display.

Abstract: Described is a system for inferring the location of users in online social media platforms using social network analysis. A social network is first extracted from data from at least one social media platform. A mapping is generated from each user to the user's estimated geographical location in the social network, resulting in an estimated location mapping. A mapping is generated from each user to the user's known geographical location, if known, resulting in a known location mapping. The estimated location mapping is updated to match the known location mapping. The location for each user j in the known location mapping is updated in a current estimated location mapping. The final geographical locations of users connected with j are estimated using a geometric median metric. Finally, the final estimated geographical locations of users connected with j are mapped into the social network.

Abstract: Described is a system for inferring the location of users in online social media platforms using social network analysis. A social network is first extracted from data from at least one social media platform. A mapping is generated from each user to the user's estimated geographical location in the social network, resulting in an estimated location mapping. A mapping is generated from each user to the user's known geographical location, if known, resulting in a known location mapping. The estimated location mapping is updated to match the known location mapping. The location for each user j in the known location mapping is updated in a current estimated location mapping. The final geographical locations of users connected with j are estimated using a geometric median metric. Finally, the final estimated geographical locations of users connected with j are mapped into the social network.

Abstract: Described is a system for automated community discovery in networks with multiple relational types. The system receives a network as input. The network comprises neighbors, edges connecting the neighbors, and vertices, where edges between two vertices represent a relation. A set of pair-wise similarity comparisons is computed for all pairs of relations. Two relations are considered similar if vertices connected to the two relations share similar relations to the same set of neighbors. A relation dendrogram is created based on the set of pair-wise similarity comparisons. Then, a cut in the relation dendrogram is selected to compute a community solution, resulting in a plurality of relation dendrogram partitions. Each relation dendrogram partition represents a community. A community density criterion is computed based on a density of each community calculated with respect to edge types contained within each community. Finally, a community solution is generated that maximizes the community density criterion.

Abstract: Described is system and method for discovering important elements that drive an online discussion. The system receives, as input, a message from an online discussion related to a topic. The message is analyzed for information related to the message. A graph comprising a plurality of nodes is generated, where each of the plurality of nodes represents an entity. The graph is updated over time as additional messages from the online discussion are received, such that additional nodes are generated and linked with at least one of the plurality of nodes by paths. A closeness value for each node is determined based on the path lengths. The entities are ranked based on the closeness values, thereby providing a ranking of the entities by level of importance in the online discussion to a user.

Abstract: Described is system and method for gauging public interest in a topic using network analysis of online discussions. A message from an online discussion related to a specific topic is received as input. The message is analyzed for information related to the message, and a graph comprised of nodes is generated where each node represents information related to the message, such as user name, location, hyperlinks, and annotations. The graph is updated over time as additional messages from the online discussion are received. Additional nodes are generated and linked with at least one of the existing nodes in the graph to form at least one connected component. A normalized diameter of the largest connected component in the graph is determined, and a level of collective focus in the online discussion related to the topic based on the normalized diameter is output to a user.

Abstract: Described is a system for automated collaborative behavior analysis using temporal motifs. The system receives an input documents and change log files of a collaborative media, where the documents are continuously edited by multiple authors and where edits are recorded in the change log files, such as Wikipedia. A type of editing behavior by the authors of a given document is identified, and the edits made to the document are analyzed. The system reports how the authors interacted in a collaboration process, resulting in a set of reported author interactions. From the set of reported author interactions, a set of author interactions that are most and least significant in the collaboration process are identified. Then, based on the set of identified author interactions, future effects on documents of the collaborative media are estimated.

Abstract: A system provides a task scheduler to define a priority queue with at least one window and a queue-window key. Each window is an ordered collection of tasks in a task pool of the priority queue and is identified by the queue-window key. The task scheduler sets a task-window key equal to a user-window key when the user-window key is greater than the minimum queue-window key. The task scheduler can further set the task-window key equal to the minimum queue-window key when the user-window key is less than the minimum queue-window key. A maximum task limit per user for each window and a priority increment for the user-window key are further applied to ensure fair scheduling.

Abstract: One embodiment may estimate the processing time of tasks requested by an application by maintaining a state-model for the application. The state model may include states that represent the tasks requested by the application, with each state including the average run-time of each task. In another embodiment, a state model may estimate which task is likely to be requested for processing after the current task is completed by providing edges in the state model connecting the states. Each edge in the state model may track the number of times the application transitions from one task to the next. Over time, data may be gathered representing the percentage of time that each edge is from a state node. Given this information, the scheduler may estimate the CPU cost of the next task based on the current state, the most likely transition, and the cost of the predicted next task.

Abstract: One embodiment may estimate the processing time of tasks requested by an application by maintaining a state-model for the application. The state model may include states that represent the tasks requested by the application, with each state including the average run-time of each task. In another embodiment, a state model may estimate which task is likely to be requested for processing after the current task is completed by providing edges in the state model connecting the states. Each edge in the state model may track the number of times the application transitions from one task to the next. Over time, data may be gathered representing the percentage of time that each edge is from a state node. Given this information, the scheduler may estimate the CPU cost of the next task based on the current state, the most likely transition, and the cost of the predicted next task.

Abstract: A system provides a task scheduler to define a priority queue with at least one window and a queue-window key. Each window is an ordered collection of tasks in a task pool of the priority queue and is identified by the queue-window key. The task scheduler sets a task-window key equal to a user-window key when the user-window key is greater than the minimum queue-window key. The task scheduler can further set the task-window key equal to the minimum queue-window key when the user-window key is less than the minimum queue-window key. A maximum task limit per user for each window and a priority increment for the user-window key are further applied to ensure fair scheduling.