Name: Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms (en Inglés)
Price: 120930.00 CLP
Availability: InStock
Author: Kalyanaraman, Ananth ; Halappanavar, Mahantesh ; Chavarría-Miranda, Daniel
ISBN: 9781680831320

portada Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms (en Inglés)

Formato

Libro Físico

Autor

Ananth Kalyanaraman

Mahantesh Halappanavar

Daniel Chavarría-Miranda

Editorial

Now Publishers

Idioma

Inglés

N° páginas

120

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 0.6 cm

Peso

0.18 kg.

ISBN13

9781680831320

Categorías

Ingeniería eléctrica
Ingeniería de control automático
Ciencias de la computación

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms (en Inglés)

Ananth Kalyanaraman (Autor) · Mahantesh Halappanavar (Autor) · Daniel Chavarría-Miranda (Autor) · Now Publishers · Tapa Blanda

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

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Reseña del libro "Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms (en Inglés)"

Graph representations are pervasive in scientific and social computing. They serve as vital tools to model the interplay between different interacting entities. This monograph delves into the problem of community detection, which is one of the most widely used graph operations toward scientific discovery. Community detection refers to the process of identifying tightly-knit subgroups of vertices in a large graph. These sub-groups (or communities) represent vertices that are tied together through common structure or function. Identification of communities could help in understanding the modular organization of complex networks. However, owing to large data sizes and high computational costs, performing community detection at scale has become increasingly challenging. This monograph presents a detailed review and analysis of some of the leading computational methods and implementations developed for executing community detection on modern day multicore and manycore architectures. The intention is to: a) define the problem of community detection and highlight its scientific significance; b) relate to challenges in parallelizing the operation on modern day architectures; c) provide a detailed report and logical organization of the approaches that have been designed for various architectures; and d) provide insights into the strengths and suitability of different architectures for community detection, and a preview into the future trends of the area. While the focus is on community detection, the challenges, and techniques to overcome the challenges, transcend to several other graph problems that have applications in science and data analytics.