Abstract: Aspects of the subject disclosure are directed towards managing sponsored online content based upon advertiser behavior. Defining mini-markets to represent such advertiser behavior may be accomplished by clustering queries that generate revenue from one or more campaigns. Query revenue data between queries and a set of campaigns may be used to determine such mini-markets. To illustrate, a query whose highest revenue is attributed to a campaign may be selected for that campaign's mini-market. When that query is entered as a search term, the campaign's mini-market helps allocate space for advertisements.

Abstract: One or more all-distances sketches are generated for nodes in a graph. An all-distances sketch for a node includes a subset of the nodes of the graph, and a shortest distance between the node and each of the nodes in the subset of nodes. The generated all-distances sketches are used to estimate the closeness similarity of nodes. The estimated closeness similarity can be used for targeted advertising or for content item recommendation, for example.

Abstract: Customizable route planning is a technique for computing point-to-point shortest paths in road networks. It includes three phases: preprocessing, metric customization, and queries. A graphics processing unit may be used, e.g., in the metric customization phase, to make customization even faster, enabling a wide range of applications including highly dynamic applications and on-line personalized cost functions.

Abstract: Distance query techniques are provided that are robust to network structure, scale to large and massive networks, and are fast, straightforward, and efficient. A hierarchical hub labeling (HHL) technique is described to determine a distance between two nodes or vertices on a network. The HHL technique provides indexing by ordering vertices by importance, then transforming the ordering into an index, which enables fast exact shortest-path distance queries. The index may be compressed without sacrificing its correctness.

Abstract: A graph that includes multiple nodes and edges is received. Multiple instances of the graph are generated by randomly instantiating the edges according to either a binary independent cascade model or a randomized edge length independent cascade model. Where the binary independent cascade model is used, combined reachability sketches are generated for each node across all instances of the graph. Where the randomized edge length independent cascade model is used, combined all-distances sketches are generated for each node across all instances of the graph. Depending on which model is used, the combined reachability or all-distances sketches are used to estimate the influence of nodes in the graph or to estimate a subset of nodes from a graph of a specified size with a maximum influence using a greedy algorithm.

Abstract: A graph that includes multiple nodes and edges is received. Multiple instances of the graph are generated by randomly instantiating the edges according to either a binary independent cascade model or a randomized edge length independent cascade model. Where the binary independent cascade model is used, combined reachability sketches are generated for each node across all instances of the graph. Where the randomized edge length independent cascade model is used, combined all-distances sketches are generated for each node across all instances of the graph. Depending on which model is used, the combined reachability or all-distances sketches are used to estimate the influence of nodes in the graph or to estimate a subset of nodes from a graph of a specified size with a maximum influence using a greedy algorithm.

Abstract: One or more all-distances sketches are generated for nodes in a graph. An all-distances sketch for a node includes a subset of the nodes of the graph, and a shortest distance between the node and each of the nodes in the subset of nodes. The generated all-distances sketches are used to estimate the closeness similarity of nodes. The estimated closeness similarity can be used for targeted advertising or for content item recommendation, for example.

Abstract: A point-to-point shortest path technique supports real-time queries and fast metric update or replacement (metric customization). Determining a shortest path between two locations uses three stages: a preprocessing stage, a metric customization stage, and a query stage. Extensions to the customizable route planning (CRP) technique for routing are provided. These extensions include, for example, the computation of alternative routes, faster techniques for unpacking shortcuts, efficient query techniques for batched shortest path (one-to-many, many-to-many, and points of interest) determinations, and determining routes and alternative routes using traffic information.

Abstract: Customizable route planning is a technique for computing point-to-point shortest paths in road networks. It includes three phases: preprocessing, metric customization, and queries. A graphics processing unit may be used, e.g., in the metric customization phase, to make customization even faster, enabling a wide range of applications including highly dynamic applications and on-line personalized cost functions.

Abstract: A graph that includes multiple nodes and edges is received. Multiple instances of the graph are generated by randomly instantiating the edges according to either a binary independent cascade model or a randomized edge length independent cascade model. Where the binary independent cascade model is used, combined reachability sketches are generated for each node across all instances of the graph. Where the randomized edge length independent cascade model is used, combined all-distances sketches are generated for each node across all instances of the graph. Depending on which model is used, the combined reachability or all-distances sketches are used to estimate the influence of nodes in the graph or to estimate a subset of nodes from a graph of a specified size with a maximum influence using a greedy algorithm.

Abstract: Distance query techniques are provided that are robust to network structure, scale to large and massive networks, and are fast, straightforward, and efficient. A hierarchical hub labeling (HHL) technique is described to determine a distance between two nodes or vertices on a network. The HHL technique provides indexing by ordering vertices by importance, then transforming the ordering into an index, which enables fast exact shortest-path distance queries. The index may be compressed without sacrificing its correctness.

Abstract: A distributed data-parallel execution (DDPE) system splits a computational problem into a plurality of sub-problems using a branch-and-bound algorithm, designates a synchronous stop time for a “plurality of processors” (for example, a cluster) for each round of execution, processes the search tree by recursively using a branch-and-bound algorithm in multiple rounds (without inter-processor communications), determines if further processing is required based on the processing round state data, and terminates processing on the processors when processing is completed.

Abstract: Aspects of the subject disclosure are directed towards managing sponsored online content based upon advertiser behavior. Defining mini-markets to represent such advertiser behavior may be accomplished by clustering queries that generate revenue from one or more campaigns. Query revenue data between queries and a set of campaigns may be used to determine such mini-markets. To illustrate, a query whose highest revenue is attributed to a campaign may be selected for that campaign's mini-market. When that query is entered as a search term, the campaign's mini-market helps allocate space for advertisements.

Abstract: Techniques are described for graph partitioning, and in particular, graph bisection. A lower bound is provided that is computed in near-linear time. These bounds may be used to determine optimum solutions to real-world graphs with many vertices (e.g., more than a million for road networks, or tens of thousands for VLSI and mesh instances). A packing lower bound technique determines lower bounds in a branch-and-bound tree, reducing the number of tree nodes. Techniques are employed to assign vertices without branching on them, again reducing the size of the tree. Decomposition is provided to translate an input graph into less complex subproblems. The decomposition boosts performance and determines the optimum solution to an input by solving subproblems independently. The subproblems can be solved independently using a branch-and-bound approach to determine the optimum bisection.

Abstract: Graph partitioning techniques are based on the notion of natural cuts. A filtering phase performs a series of minimum cut computations to identify and contract dense regions of the graph. This reduces the graph size significantly, but preserves its general structure. An assembly phase uses a combination of greedy and local search heuristics to assemble the final partition. The techniques may be used on road networks, which have an abundance of natural cuts (such as bridges, mountain passes, and ferries).

Abstract: Techniques are described for graph partitioning, and in particular, graph bisection. A combinatorial lower bound is provided that is computed in near-linear time. These bounds may be used to determine optimum solutions to real-world graphs with many vertices (e.g., more than a million for road networks, or tens of thousands for VLSI and mesh instances). Combinatorial techniques that reduce the size of the branch-and-bound search tree may use tree packing, assign vertices to trees, and use fractional assignment of vertices to trees. For graph bisection, each node of the branch-and-bound tree corresponds to a partial assignment of vertices to both cells or sets of vertices.

Abstract: A point-to-point shortest path technique supports real-time queries and fast metric update or replacement (metric customization). Determining a shortest path between two locations uses three stages: a preprocessing stage, a metric customization stage, and a query stage. Extensions to the customizable route planning (CRP) technique for routing are provided. These extensions include, for example, the computation of alternative routes, faster techniques for unpacking shortcuts, efficient query techniques for batched shortest path (one-to-many, many-to-many, and points of interest) determinations, and determining routes and alternative routes using traffic information.

Abstract: Alternative routes to an optimal route may be determined and presented to a user via a computing device. Alternative routes are selected from candidate routes that meet admissibility criteria. In an implementation, admissibility of a candidate route (in order for it to be considered an alternative route) may be determined based on three criteria: “limited sharing”, “local optimality”, and “stretch” such as “uniformly bounded stretch”. Limited sharing refers to the amount of difference between the alternative route and the optimal route, local optimality refers to lack of unnecessary detours, and uniformly bounded stretch refers to a length of the shortest path to travel between two points on the alternative route.

Abstract: Techniques are described for graph partitioning, and in particular, graph bisection. A lower bound is provided that is computed in near-linear time. These bounds may be used to determine optimum solutions to real-world graphs with many vertices (e.g., more than a million for road networks, or tens of thousands for VLSI and mesh instances). A packing lower bound technique determines lower bounds in a branch-and-bound tree, reducing the number of tree nodes. Techniques are employed to assign vertices without branching on them, again reducing the size of the tree. Decomposition is provided to translate an input graph into less complex subproblems. The decomposition boosts performance and determines the optimum solution to an input by solving subproblems independently. The subproblems can be solved independently using a branch-and-bound approach to determine the optimum bisection.

Abstract: Hub based labeling is used to determine a shortest path between two locations. Every point has a label, which consists of a set of hubs along with the distance from the point to all those hubs. The hubs are determined that intersect the two labels, and this information is used to find the shortest distance. A hub based labeling technique uses a preprocessing stage and a query stage. Finding the hubs is performed in the preprocessing stage, and finding the intersecting hubs (i.e., the common hubs they share) is performed in the query stage. During preprocessing, a forward label and a reverse label are defined for each vertex. A query is processed using the labels to determine the shortest path. Hub label compression may be used to preserve the use of labels but reduce space usage.