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Showing 1 to 4 of 4 for “"graph sampling"”.
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ScaleGPS: Scalable Graph Parallel Sampling via Data-centric Performance Engineering
Graph sampling extracts representative samples of a graph, so that approximate graph algorithms can be used in place of expensive, exact algorithms while still achieving highquality results. Thus, graph sampling plays an important role in many modern graph-based applications, such as graph machine …
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Approximate failure recovery in distributed graph processing systems
Distributed graph processing systems are an emerging area of big data systems. As graphs continue to grow in size and prevalence, these systems must become faster and more scalable. However, after failures, distributed graph processing systems either largely rely on proactive fault tolerance …
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Interpretable Network Representations
<p>Networks (or interchangeably graphs) have been ubiquitous across the globe and within science and engineering: social networks, collaboration networks, protein-protein interaction networks, infrastructure networks, among many others. Machine learning on graphs, especially network representation …
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The Polyhedral Geometry of Graphical Designs
A graphical design is a quadrature rule for a graph. That is, a graphical design is a subset of graph vertices for which the global averages of certain Laplacian eigenvectors are equal to weighted averages of these vectors over the design subset. This definition was inspired by classical quadrature …