Sentence examples for partition graphs from inspiring English sources

We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures.

K-means clustering [ 15] aims to partition graphs to clusters that minimize the within-cluster sum of squares.

By finding these dense, enriched "fuzzy clusters," or enriched quasi-cliques, we hope to achieve superior precision and coverage over conventional hard clustering techniques, which heuristically partition graphs into non-overlapping subgraphs.

If we could understand how to partition graphs in this way and how such partitioning simplified the steady-state probabilities, then we might have a means to address the complexity problem.

To address this efficiency issue, we partition graph G based on its summary graph extracted from the Delaunay triangulation of a set of selected vertices.

To partition graph G in a number of subsets the normalized cut-cost clustering method according to Shi and Malik [20] was used.

We combine the foreground/background mask with non-adjacent super-voxel regions to generate a volume partition graph over the set of super-voxels.

This definition forms an MRF (or equivalently a partition graph) over the foreground voxels (Fig. 3D), where we can directly impose smoothness constraints to calculate optical flow.

A key idea in our approach is to generate a volume partition graph over the foreground voxels, and to perform optical flow directly on that model instead of computing it at the voxel level.

To keep low the overhead of partitioning graphs, even when processing the ever-increasing modern graphs, many previous studies use lightweight streaming graph-partitioning policies.

For all distributed graph-processing systems, partitioning graphs is a key part of processing and an important aspect to achieve good processing performance.