Sentence examples for graphs with real from inspiring English sources

Our algorithm solves the APSP problem for weighted directed graphs, with real (positive or negative) weights, up to an additive error of ϵ.

Several new parallel algorithms for the single source shortest paths (SSSP) and for the negative cycle detection (NCD) problems on directed graphs with real edge weights and given by adjacency list are developed, analyzed, and experimentally compared.

The system is implemented using Spark GraphX tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time.

Performance evaluation by experiments with real graph datasets shows that the proposed method can find more effective top-k answers than the previous approaches and provides acceptable and scalable execution performance for various types of keyword queries on large graph databases.

Yet the distribution of node degrees [12] of generated random graphs must fit with real-world power grid topologies, and this must be achieved without consuming excessive time in the process of generating random graphs.

With this device, environmentally conscious people can use the C# application that we developed to monitor their energy usage with real time graphs and data display.

The algorithm is shown to perform well on simulated data and on real-world graphs with known overlapping communities.

We start with querying big real-life graphs with constrained resources, in order to provide small businesses with the benefit of big graph analyses.

Our experimental study on several realistic power grid topologies proves that the proposed algorithm can quickly generate a large number of random graphs with the topology characteristics of real-world power grid.

Furthermore, in[7], a graph model based cooperative algorithm is proposed and tested with real measurements.

The algorithm is tested both on real and artificial graphs with up to one million edges.