Abstract: The technology described herein builds an optimal refresh schedule by minimizing a cost function constrained by an available refresh bandwidth. The cost function receives an importance score for a content item and a change rate for the content item as input in order to optimize the schedule. The cost function is considered optimized when a refresh schedule is found that minimizes the cost while using the available bandwidth and no more. The technology can build an optimized schedule to refresh content with incomplete change data, content with complete change data, or a mixture of content with and without complete change data. It can also re-learn content item change rates from its own schedule execution history and re-compute the refresh schedule, ensuring that this schedule takes into account the latest trends in content item updates.

Abstract: The technology described herein builds an optimal refresh schedule by minimizing a cost function constrained by an available refresh bandwidth. The cost function receives an importance score for a content item and a change rate for the content item as input in order to optimize the schedule. The cost function is considered optimized when a refresh schedule is found that minimizes the cost while using the available bandwidth and no more. The technology can build an optimized schedule to refresh content with incomplete change data, content with complete change data, or a mixture of content with and without complete change data. It can also re-learn content item change rates from its own schedule execution history and re-compute the refresh schedule, ensuring that this schedule takes into account the latest trends in content item updates.

Abstract: The technology described herein builds an optimal refresh schedule by minimizing a cost function constrained by an available refresh bandwidth. The cost function receives an importance score for a content item and a change rate for the content item as input in order to optimize the schedule. The cost function is considered optimized when a refresh schedule is found that minimizes the cost while using the available bandwidth and no more. The technology can build an optimized schedule to refresh content with incomplete change data, content with complete change data, or a mixture of content with and without complete change data. It can also re-learn content item change rates from its own schedule execution history and re-compute the refresh schedule, ensuring that this schedule takes into account the latest trends in content item updates.

Abstract: A “Stochastic Clustering-Based Network Generator” enables rapid formation of an interconnected hierarchical network structure from an arbitrary number of agents via an iterative turn-based coalescence process. Given N agents wishing to coalesce into one hierarchical network, a turn-based process allows each agent (or the head of each hierarchical cluster of agents), to randomly decide whether to issue or listen for merge requests in each round. Issuing a request amounts to contacting a randomly chosen agent with a merge request. Given multiple received requests, a cluster head will randomly accept one request for a merge received by any agent in that cluster. The requesting cluster then merges as a hierarchical child of the accepting cluster. In a related embodiment, given multiple merge requests, the request from the smallest cluster is accepted. In further embodiments, ties of the smallest cluster size are broken based on various options.

Abstract: A “Stochastic Clustering-Based Network Generator” enables rapid formation of an interconnected hierarchical network structure from an arbitrary number of agents via an iterative turn-based coalescence process. Given N agents wishing to coalesce into one hierarchical network, a turn-based process allows each agent (or the head of each hierarchical cluster of agents), to randomly decide whether to issue or listen for merge requests in each round. Issuing a request amounts to contacting a randomly chosen agent with a merge request. Given multiple received requests, a cluster head will randomly accept one request for a merge received by any agent in that cluster. The requesting cluster then merges as a hierarchical child of the accepting cluster. In a related embodiment, given multiple merge requests, the request from the smallest cluster is accepted. In further embodiments, ties of the smallest cluster size are broken based on various options.

Abstract: Providing for local graph partitioning using an evolving set process is disclosed herein. By way of example, a computer processor can be configured to execute local partitioning based on evolving set instructions. The instructions can be employed to transition a set of analyzed vertices of a graph until a segment of the graph with small conductance is identified. A transitioning algorithm can expand or contract the analyzed set of vertices based on characteristics of vertices at a boundary of the analyzed set. Accordingly, as the set of analyzed vertices becomes large, significant processing efficiency is gained by employing the characteristics of boundary vertices to transition the set or determine conductance, rather than all vertices of the analyzed set.

Abstract: Providing a recommendation to a group of networked members is disclosed. The recommendation is provided to the group collectively, and is based on trust relationships between the members of the network. In an example embodiment, the network is a social network. Example systems and methods include a two-phase approach and a one-phase approach, each including analysis and aggregation of input associated with members of the network.

Abstract: Consumer stores typically sell products at a retail profit, which covers the cost of pre-sale, consumer-influencing services regarding the products (e.g., discussions with sales associates, product demonstrations, and consumer inspections of the products.) However, consumers may utilize these services but may purchase selected products from other retailers that do not provide pre-sale services, and that may accordingly offer lower prices. This tactic may cause lost sales for consumer stores, and eventually store failures. Instead, the consumer store may charge the supplier an influence fee for consumer-influencing services based on a per-service price established between the supplier and the consumer store and consumer influence metrics tracked by a computer.

Abstract: Providing for local graph partitioning using an evolving set process is disclosed herein. By way of example, a computer processor can be configured to execute local partitioning based on evolving set instructions. The instructions can be employed to transition a set of analyzed vertices of a graph until a segment of the graph with small conductance is identified. A transitioning algorithm can expand or contract the analyzed set of vertices based on characteristics of vertices at a boundary of the analyzed set. Accordingly, as the set of analyzed vertices becomes large, significant processing efficiency is gained by employing the characteristics of boundary vertices to transition the set or determine conductance, rather than all vertices of the analyzed set.

Abstract: A tournament-style gaming scenario exploration system and method for interactively exploring current and future scenarios of a tournament and associated pick'em pool. The system and method include a prediction module (including a game constraint sub-module), and a key event detection module. Embodiments of the prediction module include a binary integer that represents tournament outcomes. The prediction module generates predictions of tournament outcomes using an exhaustive or a sampling technique. The sampling technique includes random sampling, where the tournament bracket is randomly sampled, and a weighted sampling technique, which sample portions of the tournament bracket more densely than others areas. Embodiments of the game constraint sub-module allow real-world results constraints and user-supplied constraints to be imposed on the tournament outcomes.