Name: An In-Depth Analysis of Stochastic Kronecker Graphs (en Inglés)
Price: 85387.00 COP
Availability: InStock
Author: United States Department of Energy
ISBN: 9781502489050

portada An In-Depth Analysis of Stochastic Kronecker Graphs (en Inglés)

Formato

Libro Físico

Autor

United States Department of Energy

Editorial

Createspace Independent Publishing Platform

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

25.4 x 17.8 x 0.2 cm

Peso

0.08 kg.

ISBN13

9781502489050

Categorías

Tecnología: cuestiones generales

An In-Depth Analysis of Stochastic Kronecker Graphs (en Inglés)

United States Department of Energy (Autor) · Createspace Independent Publishing Platform · Tapa Blanda

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "An In-Depth Analysis of Stochastic Kronecker Graphs (en Inglés)"

Graph analysis is playing an increasingly important role in science and industry. Due to numerous limitations in sharing real-world graphs, models for generating massive graphs are critical for developing better algorithms. In this article, we analyze the stochastic Kronecker graph model (SKG), which is the foundation of the Graph500 supercomputer benchmark due to its favorable properties and easy parallelization. Our goal is to provide a deeper understanding of the parameters and properties of this model so that its functionality as a benchmark is increased. We develop a rigorous mathematical analysis that shows this model cannot generate a power-law distribution or even a lognormal distribution. However, we formalize an enhanced version of the SKG model that uses random noise for smoothing. We prove both in theory and in practice that this enhancement leads to a lognormal distribution. Additionally, we provide a precise analysis of isolated vertices, showing that the graphs that are produced by SKG might be quite different than intended. For example, between 50% and 75% of the vertices in the Graph500 benchmarks will be isolated. Finally, we show that this model tends to produce extremely small core numbers (compared to most social networks and other real graphs) for common parameter choices.